The DOI matches its Crossref record; 48 of the 50 references it cites were found in a registry or an index; 21 of the 22 sources its claims cite were checked for support.
Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Open accessAbstract
Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer's disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.
Overview
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Written from the paper’s full text.
TL;DR
Comparing single-cell and single-nucleus RNA-seq of human microglia from four subjects, most genes had similar abundance, but a small set (~1%) was depleted in nuclei and enriched for microglial activation genes. The authors conclude snRNA-seq is not suited for detecting cellular activation states in human microglia.
Key findings
- Most genes showed similar normalized abundance in microglial cells and nuclei: 98.6% fell along the diagonal (Pearson’s correlation coefficient = 0.92, p < 2.2e-16). However, 246 genes (1.1%) were less abundant in nuclei (fold change < −2, padj < 0.05), and 67 genes (0.3%) were more abundant in nuclei (fold change > 2, padj < 0.05).
- Nuclei-depleted genes were enriched for microglial activation signatures: 45 of 257 orthologous DAM genes (NES = −2.16, padj = 3.6e-05), 28 of 200 orthologous ARM genes (NES = −2.01, padj = 3.6e-05), LPS-upregulated genes (NES = −1.86, padj = 3.6e-05), and Mathys et al. Mic1 markers (NES = −2.14, padj = 3.6e-05). Examples included APOE, CST3, SPP1, and CD74; the abstract states this comprises 18% of previously identified microglial-disease-associated genes.
- In public datasets, cell-abundant genes were depleted in other single-nucleus microglia relative to single-cell microglia (mean microglial Z score 7.95 for cells-to-nuclei, versus 0.01 for cell-to-cell and 0.81 for nuclei-to-nuclei comparisons). Reanalysis of Gerrits et al. data found the same nuclei-depleted pattern when comparing fresh cells to frozen nuclei, but little difference between fresh cells and fresh nuclei.
- snRNA-seq recovered major cell types from human tissue: 37,060 nuclei yielded 7 major cell types, including microglia (11.3%), but clustering did not convincingly recover differences between other microglial subpopulations.
Methods
- Paired scRNA-seq of FACS-sorted microglia and snRNA-seq from the same four human temporal cortex samples (epilepsy surgery; ages 7 male, 20, 24, and 50 female) using the 10X Genomics single-cell gene expression v2 kit. After preprocessing, 3,927 microglial nuclei remained; differential abundance analysis used 3,721 nuclei and 14,435 cells after discarding non-microglial clusters.
- Differential abundance was performed with Seurat using a two-sided Wilcoxon rank sum test with Bonferroni correction; genes with padj < 0.05 and fold change > |2| were considered significant. Functional analysis used GSEA (fgsea) against ranked log fold changes and gene sets from prior studies.
- Public dataset comparisons used the EWCE package and a MicroglialDepletion package with bootstrapped Z scores across 8 public single-cell or single-nucleus datasets; Gerrits et al. data were downloaded from GEO (GSE135618) and reanalyzed for fresh versus frozen nuclei.
Limitations
- Stated in the paper: the study used non-diseased tissue, so the authors cannot dismiss the possibility that mRNA localization distributions change in more activated microglia. They also state they cannot attribute cell-versus-nucleus abundance differences to a biological mechanism versus a technical artifact, and the reason for differences between fresh and frozen nuclei is unclear.
- Stated in the paper: deeper sequencing or increased sample size could possibly compensate for lack of sensitivity, but the sparse nature of snRNA-seq and high human heterogeneity suggest this may remain a problem.
- Evident from the text: the primary comparison relies on only four non-AD epilepsy surgery subjects, with one patient sample removed due to poor quality, so the findings about disease-associated activation are inferred from gene-set enrichment rather than direct AD patient snRNA-seq in this study.
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Claims the paper makes
31 Own result · 11 Supported · 10 Own method · 7 Partly supported · 6 Own interpretation · 1 Mixed support · accuracy not yet measured
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease.”
- Also stated in: § Introduction · hedged
“a number of high-profile snRNA-seq studies of microglia in human AD (Del-Aguila et al., 2019; Grubman et al., 2019; Mathys et al., 2019) have been unable to recover an equivalent activation signature or, possibly more revealing, a consistent activation signature.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Abstract
What the sources say
- Partly supportedcites Del-Aguila et al., 2019 sourceExperimental — accuracy not yet measured
The Results section (page unavailable) says the study did not sequence enough microglial cells to replicate the previously reported disease-associated microglia (DAM) marker findings. But it found five markers consistently associated with microglia across all three samples. The claim overgeneralizes this limited finding to a failure to detect any consistent microglial activation signature in human Alzheimer’s disease samples. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites Grubman et al., 2019 source
- Disputedcites Mathys et al., 2019 sourceExperimental — accuracy not yet measured
In “Identification of AD-associated cellular subpopulations” (page not specified), the paper reports an AD-associated human microglial subpopulation, Mic1, with immune-related marker genes that significantly overlap disease-associated microglial signatures. It also reports immunohistochemical confirmation of MHC class II–expressing microglia in AD-pathology samples. This is contrary to attributing a failure to detect a microglial activation signature to this study, though the paper does not establish consistency across all human snRNA-seq studies. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · hedged
Every source the paper cites for this claim was checked and supports it.
In the paper
“Markers of this response (referred to by Mathys et al., 2019 as “Mic1”) had a NES score of −2.14 (padj = 3.6e-05), indicating that they were depleted in nuclei (Figure 2C).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Activation Genes Identified in Human Studies of AD Are Depleted in Human Nuclei
What the sources say
- Supportedcites Mathys et al., 2019 source
The Identification of AD-associated cellular subpopulations section identifies Mic1 as an AD-associated human microglial subpopulation and discusses its marker genes. The citation supports the name and identification of the markers; the NES score and depletion in nuclei are presented as the citing paper’s Figure 2C result, not as a finding attributed to Mathys et al. (LLM, conf=0.92)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Genes upregulated by lipopolysaccharide (LPS) stimulation in mice (Gerrits et al., 2019) also showed depletion in nuclei (NES = −1.86, padj = 3.6e-05; Figure 2C; Figure S2F).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Activation Genes Identified in Mouse Models of AD Are Depleted in Human Nuclei
What the sources say
- Supportedcites Gerrits et al., 2019 source
The Results section (Comparison of nuclear and whole-cell transcriptome by bulk sequencing) reports genes upregulated after LPS treatment in mice, supporting the citation’s identification of that gene set. The depletion statistic and Figure 2C are presented as the citing paper’s own result, not as a finding attributed to Gerrits et al. (LLM, conf=0.91)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The study identified 28 DAM genes as marker genes of the Mic1 response cluster (shown in orange in Figure 2F); however, most DAM genes were not identified as activation genes using their snRNA-seq protocol (purple in Figure 2F).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Activation Genes Identified in Human Studies of AD Are Depleted in Human Nuclei
What the sources say
- Partly supportedcites Mathys et al., 2019 sourceExperimental — accuracy not yet measured
The Identification of AD-associated cellular subpopulations section reports a significant overlap between Mic1 markers and mouse disease-associated microglia genes, while describing the states as distinct. It does not say that most DAM genes were missed or quantify how many were not identified as activation genes by its snRNA-seq analysis. Page numbers were not available in our copy. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“we found a strong depletion of genes associated with mouse microglial activation: 45 of 257 orthologous DAM genes (Keren-Shaul et al., 2017; NES = −2.16, padj = 3.6e-05; Figures 2C and 2D)”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Activation Genes Identified in Mouse Models of AD Are Depleted in Human Nuclei
What the sources say
- Supportedcites Keren-Shaul et al., 2017 source
The Results identify disease-associated microglia (DAM) in an AD mouse model and describe their distinctive gene-expression program. The citation identifies the DAM genes used in the citing authors’ analysis; the depletion of 45 of 257 orthologous genes is presented as the citing authors’ own finding, not as a result from this source. (LLM, conf=0.86)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“cytoplasmic RNA (defined by Bahar Halpern et al., 2015) was clearly enriched among genes found to be more abundant in cells (NES = −2.03, padj = 3.6e-05)”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Supportedcites Bahar Halpern et al., 2015 source
The citation identifies the source of the ‘cytoplasmic RNA’ gene category, not the source of the citing paper’s enrichment result. In Results (‘RNA Sequencing of Cell Fractions Reveals Broad Nuclear Localization of mRNA in Beta Cells and Liver’), the paper compares nuclear and cytoplasmic mRNA levels and refers to a group of ‘the most cytoplasmic genes.’ The NES and adjusted p-value are results reported by the citing paper, not attributed to this source. (LLM, conf=0.91)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Genes with shorter coding sequences (CDSs) were depleted in nuclei (NES = −1.88, padj = 3.6e-05)”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Partly supportedcites Bakken et al., 2018 sourceExperimental — accuracy not yet measured
The Results section, “Comparison of nuclear and whole cell transcriptomes” (page unavailable), reports that nucleus-enriched genes were longer on average than cell-enriched genes, which supports a general association with gene length. It does not establish depletion specifically for shorter coding sequences or report the cited NES and adjusted p-value; it also cautions that the length difference may reflect intronic reads. (LLM, conf=0.82)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Finally, the genes defined by Gerrits et al. (2019) as cellular enriched in a differential analysis of microglial cells versus (fresh) nuclei in humans were also enriched in cells in our data, showing a NES score of −1.92 (padj = 3.6e-05).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Supportedcites Gerrits et al., 2019 source
The Results section on human microglia compares cells with fresh nuclei and reports differentially expressed genes for two donors; it also notes lower detection of mitochondrial and ribosomal genes in nuclei. The NES score and adjusted p value describe the citing authors’ own data, not a result attributed to Gerrits et al. The supplied passages do not give page numbers. (LLM, conf=0.87)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“and 28 of 200 orthologous ARM genes (Sala Frigerio et al., 2019; NES = −2.01, padj = 3.6e-05; Figures 2C and 2E), confirming that mouse microglial activation genes were less abundant in nuclei.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Activation Genes Identified in Mouse Models of AD Are Depleted in Human Nuclei
What the sources say
- Supportedcites Sala Frigerio et al., 2019 source
The Results section (‘Aging and Aβ Deposition Induce Similar Responses in Microglia’; page not available) identifies ARMs and describes their characteristic genes. The citation identifies the ARM gene set; the depletion in nuclei, the 28-of-200 count, and the enrichment statistics are presented as the citing paper’s own analysis, not as findings attributed to Sala Frigerio et al. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Droplet-based methods do not appear to suffer from length bias (Phipson et al., 2017); thus, we cannot not attribute differences in capture efficiency to a preference for capturing longer genes.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
What the sources say
- Partly supportedcites Phipson et al., 2017 sourceExperimental — accuracy not yet measured
The Results section ('UMI-based protocols do not suffer from gene length bias'; page unavailable) reports fairly uniform counts and dropout across gene lengths in an InDrop droplet-based dataset. The paper draws its broader conclusion for UMI-based protocols, not droplet-based methods generally; it does not establish that other droplet methods lack length bias. (LLM, conf=0.84)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
- Probably not a substantial claim. 28
- It may state two separate claims as one. 12
Every source the paper cites for this claim was checked and supports it.
In the paper
“as was mitochondrial mRNA (NES = −1.69, padj = 2.7e-04, gene set extracted from Ensembl’s BioMart; Zerbino et al., 2018).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Supportedcites Zerbino et al., 2018 source
The citation identifies Ensembl’s BioMart as the source of the gene set, not as the source of the enrichment result. The Introduction describes BioMart as a tool Ensembl supports for data-analysis workflows; the NES and adjusted p-value are the citing authors’ results. (LLM, conf=0.92)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“RNA of genes coding for ribosomal proteins was also depleted in nuclei (NES = −2.37, padj = 3.6e-05), as previously described (Habib et al., 2017).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Supportedcites Habib et al., 2017 source
In Online Methods (Computational Data Analysis), the paper reports that many ribosomal protein RNAs were up-regulated in 3T3 cells relative to nuclei. This supports the cited qualitative statement that they are depleted in nuclei; the NES and adjusted p-value are results of the citing paper, not attributed to Habib et al. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“whereas longer CDSs were enriched (NES = 1.91, padj = 3.6e-05), as already observed in earlier snRNA-seq studies (Bakken et al., 2018).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Partly supportedcites Bakken et al., 2018 sourceExperimental — accuracy not yet measured
In Results, under “Comparison of nuclear and whole cell transcriptomes” (page unavailable), Bakken et al. report that nucleus-enriched genes were longer than cell-enriched genes when expression was calculated from intronic and exonic reads. They do not establish that the coding sequences specifically were longer; they caution that long introns may account for the apparent enrichment. The NES and adjusted P-value in the citing sentence are not presented as Bakken et al.’s results. (LLM, conf=0.88)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For our single-cell protocol, we performed dissociation at 4°C to mitigate potential activation (Mancuso et al., 2019), so it is unlikely the dissociation procedures account for the differences.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
What the sources say
- Partly supportedcites Mancuso et al., 2019 sourceExperimental — accuracy not yet measured
The Methods section, “Isolation of human and mouse microglia from the mouse brain” (page unavailable), describes tissue dissociation followed by centrifugation and antibody labeling at 4°C. It does not say dissociation itself was performed at 4°C or that cold processing mitigated activation. The conclusion about the citing authors’ own differences is not attributed to Mancuso et al. (LLM, conf=0.82)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This is supported by the enrichment of synaptosome genes (NES = 1.82, padj = 3.6e-05, Figure 2A; Hafner et al., 2019) and ambient RNA—mRNA originating not from intact cells/nuclei but from free-floating transcripts (Macosko et al., 2015)—(NES = 1.71, padj = 9.2e-05; Figure 2A; Figure S2D) within the nucleus-abundant genes.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
What the sources say
- Supportedcites Hafner et al., 2019 source
The citation to Hafner et al. identifies the synaptosome genes used in the citing paper's enrichment analysis. In 'The presynaptic transcriptome of vGLUT1+ terminals,' the source reports RNA sequencing of synaptosomes and identifies transcripts enriched in sorted synaptosomes. The nucleus-abundant enrichment statistic and ambient-RNA result are presented as the citing paper's own analysis, not as findings attributed to Hafner et al. (LLM, conf=0.91)
- Supportedcites Macosko et al., 2015 source
The Macosko et al. citation is attached to the definition of ambient RNA, not to the citing paper’s enrichment result. In Results, under “The single cell accuracy and sensitivity of Drop-Seq libraries” (page unavailable), the source describes ambient RNA as RNA present in the cell suspension, presumably from cells damaged during preparation. This supports the description of transcripts not originating from intact cells or nuclei. (LLM, conf=0.91)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In agreement with the results of Gerrits et al. (2019), when we plotted the normalized abundance of fresh nuclei versus cells, we saw very little difference between the two populations (Figure S3C).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Comparisons with Previous Studies
What the sources say
- Supportedcites Gerrits et al., 2019 source
The Discussion reports that fresh human microglia nuclei and cells clustered very similarly. The Results likewise describe fresh nuclei as a good proxy for the corresponding cells. The citing authors’ normalized-abundance plot is their own analysis; the attributed point—that fresh nuclei and cells showed little difference—agrees with the source. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our lab has previously used the same protocol to study microglial activation in mice, and no activation due to technical artifacts was seen (Sala Frigerio et al., 2019).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
What the sources say
- Partly supportedcites Sala Frigerio et al., 2019 sourceExperimental — accuracy not yet measured
The Results and STAR★METHODS (page not provided) describe studying microglial activation in mice and keeping tissue cold to minimize activation caused by isolation. They do not report that no activation due to technical artifacts was observed; minimizing a risk is not evidence that it was absent. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The improved v3 chemistry yields a higher number of reads and genes per cell, as shown in a comparison using peripheral blood mononuclear cells (PBMCs) (Ding et al., 2020).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
What the sources say
- Partly supportedcites Ding et al., 2020 sourceExperimental — accuracy not yet measured
The Results section, “Similar relative ranking of method sensitivity across experiments” (page unavailable), reports that 10x Chromium v3 detected the most genes per PBMC cell among the high-throughput methods. It does not report that v3 yielded more reads per cell; its higher UMI count is not a read count. The claim also omits the high-throughput-methods qualifier. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, 3,248 genes were only detected in cells, and 5,068 genes were exclusively detected in nuclei.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A second population of 67 genes (0.3%) was found to be more abundant in nuclei (fold change > 2, padj < 0.05; red in Figure 1A).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
plaincorrelationaln = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A comparison of single-nucleus and single-cell clustering suggested that cytokine clusters and macrophage clusters recovered well by using single-nucleus methods”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > snRNA-Seq Recovers Major Cell Types from Human Tissue but Not Microglial State
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the frozen nuclei dataset, microglial clusters (clusters 3,5 and 6 in Figures S3A and S3B) were identified using known markers including P2RY12, CSF1R, CX3CR1 (resulting in 2,659 microglial nuclei), and extracted for downstream analysis.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plainn = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our nuclei were able to recover a cytokine response cluster (CRM), marked by CCL3, CCL4, and an activation-like cluster, equivalent to the “in vitro microglia” identified in the original study (original markers included APOC1, GPNMB, SPP1, APOE).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster Nuc4 showed similarities with the “in vitro 2” cluster, suggesting it could be a cluster of activation, however all 5 overlapping genes were mitochondrial genes.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The cell macrophage (CAM) and cell cytokine (CRM) clusters showed the largest overlaps with Nuc1 and Nuc5 (27 and 24 of 40 markers, respectively).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster Nuc3 markers RPS12, TPT1, FTL, RPS18 and EEF1A1 also appeared as markers of “in vitro 2.””
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified 7 main cell types in 37,060 nuclei: oligodendrocytes (ODC, 34.0%), excitatory neurons (27.0%), interneurons (11.3%), oligodendrocyte precursors (OPC 9,4%), microglia (11.3%), astrocytes (6.0%), and endothelial cells (1.1%).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This resulted in 1,304 mean reads per cell, compared with the original count of 3,979 reads per cell.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other clusters only showed overlaps of between 1 and 5 genes.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The dataset consisted of 37,060 nuclei, with a mean UMI count of 4,305 counts per nucleus, and 1,791 genes per nucleus.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster Nuc0 showed an overlap of 5 genes with “in vivo HM,” and cluster Nuc2 showed an overlap of 2 genes with “in vivo HM.””
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Cluster Nuc3 showed similar overlaps between “in vitro 1” and “in vitro 2” (5 genes).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified 3,721 microglia (expressing MEF2A, P2RY12, CX3CR1, CSF1R), a macrophage cluster (enriched for CD163 and MRC1, 67 nuclei), a neutrophil cluster (72 nuclei), and a cluster containing microglial as well as astrocytic markers (marked by GFAP, 68 nuclei).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After pre-processing, 3,927 nuclei remained, with a mean UMI count of 1,328 and a mean gene count of 879 genes per nucleus.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plainn = 4- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, the CAM (macrophage) cluster (CD163, MRC1), separated out from the bulk of the microglia, and was easily-recognizable based on its MS40 score.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Selecting 40 markers allowed us to align cellular and nuclear clusters in an almost one-to-one fashion (see Figure S1F).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“although snRNA-seq offers a viable alternative to scRNA-seq for the identification of cell types in tissue for which cell dissociation is problematic”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“One caveat of the present study is that we have used non-diseased tissue, and we cannot dismiss the possibility that mRNA localization distributions change in more activated populations of microglia.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure the high number of cells relative to nuclei did not affect the result (Figure S2C), we randomly sampled 3,927 cells from the count matrix.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In the fresh nuclei and cells, examination of these markers in the clustering confirmed that all cells were microglial.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
boostedn = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We confirm our results using publicly available data.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Following alignment, nuclei from one patient sample (RM101.1) were removed due to poor quality (low read and gene count).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure S1C, highlighting known microglial markers (P2RY12, CSF1R, DOCK8 and CX3CR1) as well as markers for other celltypes (MBP for oligodendrocytes, GRIA1 for neurons, AQP4 for astrocytes and CLDN5 for endothelial nuclei), confirms that we extracted only microglial clusters from the original data.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The neutrophil and ambiguous clusters were discarded, leaving only microglia and brain macrophages for downstream analysis (Figure S1D).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
scRNA-seq of whole cells provides an accurate reference for the true cellular transcriptome against which snRNA-seq can be judged.
“To compare gene abundance in single microglial cells (14,823 cells) and nuclei (3,940 nuclei), we performed a differential abundance analysis between cells and nuclei from the 4 subjects (Figure 1A).”
The paper relies on this without support.
The limitations of snRNA-seq observed with the 10X Genomics v2 kit and frozen tissue generalize to snRNA-seq generally in human disease.
“we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.”
The paper relies on this without support.
The four human subjects and non-diseased tissue are representative enough to support conclusions about human microglial activation in disease.
“One caveat of the present study is that we have used non-diseased tissue, and we cannot dismiss the possibility that mRNA localization distributions change in more activated populations of microglia.”
The paper relies on this without support.
The nuclei-depleted genes are depleted due to technical limitations of snRNA-seq rather than biological differences in nuclear mRNA localization.
“However, we are unable to attribute the differences in cellular and nuclear abundance to a biological mechanism (for instance, mRNA trafficking) or a technical artifact of the methodology.”
The authors state this as an assumption.
Nuclei with less than 700 counts from the original unfiltered raw count matrix were empty drops rather than nuclei.
“under the assumption that these were empty drops rather than nuclei”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Our study has several limitations: isotropic fractionation and read depth cut-offs may bias cell recovery based on their nuclear content; nuclear RNA may not fully capture microglial states82 or localized transcriptomic changes; and pathology burden is based on per-sample averages instead of on the spatial context of each cell.”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [82]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > snRNA-Seq Recovers Major Cell Types from Human Tissue but Not Microglial State
“interneurons (11.2%)”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > STAR★Methods > Quantification and Statistical Analysis
“interneurons (11.3%)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
“we detected 238 genes that were significantly less abundant in cells (fold change < −2, padj < 0.05; blue in Figure S2E), in comparison to the original 246 genes.”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
“we identified a group of 246 genes (1.1% of all genes detected) that was less abundant in nuclei”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
“No changes were seen in the number of genes that were underrepresented in nuclei (67).”
§ Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans > Results > Gene Expression Profiling of Human Nuclei and Cells
“A second population of 67 genes (0.3%) was found to be more abundant in nuclei”
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · cited 2×
“To assess functional enrichment among genes found to be more abundant in cells or nuclei, we ranked all genes according to log fold change (genes with a low abundance in nuclei had a negative log fold change) and performed a gene set enrichment analysis (GSEA; Subramanian et al., 2005) against gene markers from previo…”
MethodComprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111 · cited 1×
“Gene ontology (GO) sets were obtained from MSigDB (Liberzon et al., 2011; Subramanian et al., 2005).”
MethodHighly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 2×
“…enrichment of synaptosome genes (NES = 1.82, padj = 3.6e-05, Figure 2A; Hafner et al., 2019) and ambient RNA—mRNA originating not from intact cells/nuclei but from free-floating transcripts (Macosko et al., 2015)—(NES = 1.71, padj = 9.2e-05; Figure 2A; Figure S2D) within the nucleus-abundant genes.”
BackgroundInfluentialA Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493 · cited 5×
“, 2019); DAM, disease-associated microglia (Keren-Shaul et al., 2017); LPS, lipopolysaccharide (Gerrits et al.”
BackgroundInfluentialMcGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · cited 1×
“In addition, there is significant overlap between those genes involved in the mouse microglial response and AD risk genes identified in genome-wide association studies (Efthymiou and Goate, 2017; Gosselin et al., 2017; Jansen et al., 2019; Kunkle et al., 2019; Lambert et al., 2013; Marioni et al., 2018), for example,…”
BackgroundVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · cited 1×
“In situ spatial transcriptomics (STs) negates issues related to tissue dissociation and cell or nucleus isolation (Ståhl et al., 2016), while at the same time retaining spatial information.”
BackgroundFast gene set enrichment analysisPreprint
Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. N., & Sergushichev, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060012 · Cited by 3,742
Cited by
Sorted by citations
Items 201–300 of 343
Lemcke, R., Egebjerg, C., Berendtsen, N. T., Egerod, K. L., Thomsen, A. R., Pers, T. H., Christensen, J. P., & Kornum, B. R. (2023) · eLife · doi:10.7554/elife.87515 · Cited by 10
Ernst, K., Okonechnikov, K., Bageritz, J., Perera, A. A., Mallm, J., Wittmann, A., Maaß, K. K., Leible, S., Boutros, M., Pfister, S. M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97053-9 · Cited by 18
Álvarez-Arguedas, S., Mazhar, K., Wangzhou, A., Sankaranarayanan, I., Gaona, G., Lafin, J. T., Mitchell, R. B., Price, T. J., & Shiloh, M. U. (2025) · Mucosal Immunology · doi:10.1016/j.mucimm.2025.07.006 · Cited by 6
“135 138 Elucidation of the complete cell repertoire in the human adenoid will facilitate studies aimed at dissecting the earliest interactions of airway pathogens and the mucosal immune system.”
Roadblock: improved annotations do not necessarily translate into new functional insights
Hall, N., Carlyle, B. C., Haerty, W., & Tunbridge, E. M. (2021) · Genome biology · doi:10.1186/s13059-021-02542-5 · Cited by 5
“brain, outside of rarefied cellular populations [5], large-scale sequencing efforts necessarily focus either on bulk tissue, which contains a mixture of diverse cellular populations, or single nucleus sequencing, which does not necessarily reflect the total transcript pool [6].”
BackgroundImpact of non-neuronal cells in Alzheimer’s disease from a single-nucleus profiling perspective
Vu, T.-M., Hervé, V., Ulfat, A. K., Lamontagne-Kam, D., & Brouillette, J. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1208122 · Cited by 5
“Another reason would be that single-nucleus sequencing is less sensitive than single-cell sequencing to capture human microglial activation since only a very small population of genes previously identified in the mouse microglial response, such as APOE, SPP1, CD74, and CST3, has been shown to be reduced in nuclei comp…”
BackgroundThe impact of chronic pain on brain gene expression
Collier, L., Seah, C., Hicks, E. M., Holtzheimer, P. E., Krystal, J. H., Girgenti, M. J., Huckins, L. M., & Johnston, K. J. A. (2025) · Pain · doi:10.1097/j.pain.0000000000003707 · Cited by 6
“We did not perform formal enrichment tests because it was not possible to discern the total number of genes shared as background between our analyses and the Thrupp et al. gene list experiments.”
InfluentialLAG3 is not expressed in human and murine neurons and does not modulate α-synucleinopathiesPreprint
Emmenegger, M., Cecco, E. D., Hruška-Plocháň, M., Eninger, T., Schneider, M. M., Barth, M., Tantardini, E., Rossi, P. D., Bacioglu, M., Langston, R. G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.25.441302 · Cited by 5
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5
Self-citationHes, C., Tomlinson, A. J., Michielsen, L., Murdoch, H. J., Soltani, F., Kokoeva, M. V., & Sabatini, P. V. (2025) · eLife · doi:10.7554/elife.106217 · Cited by 7
“…does not appear to impact gene expression analysis for neurons in the current pipelines like the ones we used for our mouse data ( Lake et al., 2017 ), it is possible that some biological features such as microglial activation state are not fully recovered using nuclear RNA ( Thrupp et al., 2020 ).”
Li, A., Allen, P. C., Wang, Y., Cui, H., Lin, T., Sun, Y., Wang, X. M., Tan, X., Walker, A., Wang, S., et al. (2025) · medRxiv · doi:10.1101/2025.05.24.25328275 · Cited by 7
“Fourth, differences between scRNA-seq and snRNA-seq are not considered. snRNA-seq may miss important gene expression signals 12 , such as microglial activations shown by previous research 48 .”
Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact
Albertson, A. J., Winkler, E. A., Yang, A. C., Buckwalter, M. S., Dingman, A., Fan, H., Herson, P. S., McCullough, L. D., Pérez‐Pinzón, M. A., Sansing, L. H., et al. (2024) · Stroke · doi:10.1161/strokeaha.124.049001 · Cited by 4
“66 Furthermore, a substantial part of our transcriptome is not polyadenylated and not covered by currently available scRNA-seq or snRNA-seq pipelines.”
Building Immunocompetent Cerebral Organoids From a Developmental Perspective
Cuesta‐Puente, X., González-Domínguez, M., Pereira‐Iglesias, M., Perez‐Arriazu, N., Villegas‐Zafra, P., Ramos‐Gonzalez, P., Cavaliere, F., Bengoa‐Vergniory, N., & Sierra, A. (2025) · Glia · doi:10.1002/glia.70062 · Cited by 6
“…(snRNA-seq) are the standard methods to assess the transcriptional profile of oMi-croglia (Ormel et al. 2018; Sabate-Soler et al. 2022), although it should be noted that snRNA-seq will miss many cytoplasmic RNAs, and has been deem “unsuitable” to study human microglia (Thrupp et al. 2020).”
Xu, B., Bai, Y., Liu, Z., Li, J., Chen, K., Yan, Z., Wang, Y.-Y., Xu, J., Wang, H., Shi, F., et al. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.08.001 · Cited by 4
Gerakopoulos, V., Ramos, C., Müller, C., Walterskirchen, N., Vintila, S., Zotter, C., Ilg, M., Pap, A., Riss, S., Bergmann, M. M., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217678 · Cited by 4
Pecoraro, S., Verkerke, M., Sluijs, J. A., Hof, B. V. H., van der Pol, S. M. A., van Strien, M. E., van der Kant, R., de Vries, C. J. M., de Vries, H. E., van de Berg, W. D. J., et al. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.06.017 · Cited by 4
Elias, A. E., Nuñez, T. A., Kun, B., & Kreiling, J. A. (2022) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2022.10.003 · Cited by 3
Local translation in microglial processes
Vecchiarelli, H. A., & Tremblay, M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01370-z · Cited by 4
Hatai, D., Levenson, M. T., Rehan, V. K., & Allard, P. (2024) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2024.100465 · Cited by 4
Cruchaga, C., Sung, Y. J., Yang, C., Wang, F., Suhy, A., Norton, J. B., Novotny, B. C., Eteleeb, A. M., Fagan, A. M., Bateman, R. J., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-923492/v1 · Cited by 4
FX-Cell: Quantitative cell release from fixed plant tissues for single-cell genomicsPreprint
Marchant, D. B., Nelms, B. D., & Walbot, V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.11.463960 · Cited by 4
Masrori, P., Bijnens, B., Davie, K., Poovathingal, S. K., Storm, A., Hersmus, N., Fumagalli, L., Bosch, L. V. D., Fiers, M. W. E. J., Thal, D. R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.26.513909 · Cited by 4
Self-citationFortmann, S. D., Frey, B. F., Hanumanthu, V. S., Liu, S., Goldsborough, A. S., Kilchrist, K. V., Ferrell, P. B., Weaver, C. T., Grant, M. B., & Welner, R. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.13.528370 · Cited by 4
Multi-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte statePreprint
Paryani, F., Kwon, J., Ng, C. W., Madden, N., Ofori, K., Tang, A., Lü, H., Li, J., Mahajan, A., Davidson, S. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.08.556867 · Cited by 4
Myers, B. L., Brayer, K. J., Paez-Beltran, L. E., Keith, M. S., Suzuki, H., Newville, J., Anderson, R. H., Lo, Y., Mertz, C. M., Kollipara, R. K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.30.560206 · Cited by 4
Ishii, T., Wang, T., Shibata, K., Nishitani, S., Yamanashi, T., Wahba, N. E., Seki, T., Thompson, K. J., Yamanishi, K., Nishiguchi, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.13.643155 · Cited by 5
“Each cluster was annotated with cell types based on representative marker genes corresponding to major brain cell types, including microglia, astrocytes, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), endothelial cells, excitatory neurons, and inhibitory neurons 21–23 .”
Martins-Ferreira, R., Calafell-Segura, J., Chaves, J., Ciudad, L., da Silva, A. M., Costa, P., Leal, B., & Ballestar, E. (2024) · iScience · doi:10.1016/j.isci.2024.110546 · Cited by 3
Teng, T., Wu, Q., Yin, B., Zhang, J., Li, X., Zhang, L., Zhou, X., & Xie, P. (2025) · Neuroscience Bulletin · doi:10.1007/s12264-025-01412-5 · Cited by 3
“%) of genes are depleted in snRNA-seq compared to scRNA-seq [90].”
Age‐Associated Transcriptomic and Epigenetic Alterations in Mouse Hippocampus
Bilgic, M., Obata, R., Panfil, V., Zhu, Z., Saeki, M., Gotoh, Y., & Kishi, Y. (2025) · Aging Cell · doi:10.1111/acel.70233 · Cited by 5
“Specifically, single-nucleus transcriptome sequencing has been shown to fail to robustly detect microglial activation genes, leading to an underrepresentation of immune-related transcripts when compared to single-cell RNA sequencing (Thrupp et al. 2020).”
Liu, R., Yan, H., Liu, X., Xie, Y., Li, Y., Wang, Z., Huang, H., Yu, Z., Qu, W., Wang, M., et al. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03661-7 · Cited by 5
Connolly, M. G., Johnson, Z. V., Chu, L., Johnson, N. D., Buhr, T. J., McNeill, E. M., Clark, P., & Rhodes, J. S. (2025) · Genes Brain & Behavior · doi:10.1111/gbb.70019 · Cited by 3
Pavan, R. R., Diniz, F., El-Dahr, S. S., & Tortelote, G. G. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1144266 · Cited by 3
“A recent report found that single-nucleus RNA sequencing was not a suitable method to detect microglial activation associated with Alzheimer’s disease in humans (Thrupp et al., 2020).”
BackgroundMethodWakid, M., Almeida, D. M., Aouabed, Z., Rahimian, R., Davoli, M. A., Yerko, V., Leonova-Erko, E., Richard, V. R., Zahedi, R. P., Borchers, C. H., et al. (2023) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2023.100684 · Cited by 4
“Those depleted were previously implicated in microglial activation, such as APOE, SPP1, CST3, and CD74, totalling 18% of previously identified microglial-disease-associated genes (Thrupp et al., 2020).”
BackgroundUtkina, M. V., Shcherbakova, A. S., Deviatiiarov, R. M., Ryabova, A., Loguinova, M. Y., Trofimov, V., Kuznetsova, A. A., Petropavlovskiy, M., Salimkhanov, R. K., Maksimov, D. O., et al. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2024.1469955 · Cited by 4
Wakid, M., Almeida, D., Denniston, R., Chawla, A., Aouabed, Z., Davoli, M. A., Ellerbeck, K., Rahimian, R., Yerko, V., Leonova-Erko, E., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03285-6 · Cited by 4
Gupta, A., Shamsi, F., Altemose, N., Dorlhiac, G., Cypess, A. M., White, A. P., Patti, M., Tseng, Y., & Streets, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.24.435852 · Cited by 3
Chen, S., Chang, Y., Li, L., Acosta, D. M., Morrison, C. T., Wang, C., Julian, D. L., Hester, M. E., Serrano, G. E., Beach, T. G., et al. (2021) · SSRN Electronic Journal · doi:10.21203/rs.3.rs-855234/v1 · Cited by 3
Dynamic dysregulation of retrotransposons in neurodegenerative diseases at the single-cell levelPreprint
Deng, W., Citu, C., Liu, A., & Zhao, Z. (2024) · Genome Research · doi:10.1101/gr.279363.124 · Cited by 3
Schuler, H., Zhou, D., Savignac, C., Cvetkovska, V., Tse, Y.-C., Meccia, J., Lopez, J., Harutyunyan, A. S., Ragoussis, J., Bzdok, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631940 · Cited by 5
“To examine the sustained effect of psilocybin in mPFC neuronal and non-neuronal cells, we used scRNA-seq. scRNAseq presents certain advantages over single nucleus RNAseq in more accurately detecting cellular state changes and transcriptional regulation (see methods) 39 .”
Duc, P. M., Thủy, L. T. T., Hai, H., Hà, N. T. T., Anh, P. T., Ikenaga, H., Fuji, H., Yuasa, H., Matsubara, T., Huyen, V. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.12.657772 · Cited by 3
Kwon, S. H., Guo, B., Fang, C., Tippani, M., Bach, S. V., Pertea, G. M., Miller, R. A., Maguire, S. E., Iatrou, A., Eagles, N. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706214 · Cited by 5
Leveraging single-cell RNA-seq in helminthology
Mu, Y., Zumuk, C. P., Jones, M. K., & Cai, P. (2025) · Trends in Parasitology · doi:10.1016/j.pt.2025.11.003 · Cited by 3
Integrated experimental and computational workflows for single-cell transcriptomics in plants
Wang, J., zheng, S., Lu, B., Jiang, Y., Zhu, Y., Liu, Q., Gao, S., Liu, P., Yu, P., Jiang, S., et al. (2026) · Plant Methods · doi:10.1186/s13007-025-01490-6 · Cited by 2
“In mammalian tissues, scRNA-seq and snRNA-seq have shown systematic biases [4–7] and led to practical guidelines for selecting the appropriate method based on tissue type and experimental context [8–11].”
Cholesterol dysregulation in APOE4 astrocytes promotes α-synuclein pathology in miBrains
Mesentier-Louro, L. A., Goldman, C., Gaese, S., Buonfiglioli, A., Kyriakis, D., Harlock, A., Ndayisaba, A., Sartori, E. R., Uchitelev, A., Fullard, J. F., et al. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.08.001 · Cited by 6
A facile method to generate cerebral organoids from human pluripotent stem cells
Simorgh, S., Mousavi, S. A., To, S. K., Pasque, V., Wierda, K. D. B., Vervliet, T., Yeganeh, M., Pooyan, P., Chai, Y. C., Verfaillie, C. M., et al. (2023) · EXCLI journal · doi:10.17179/excli2023-6299 · Cited by 6
Unraveling the enigma: housekeeping gene Ugt1a7c as a universal biomarker for microglia
Kim, W., Kim, M.-J., & Kim, B. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1364201 · Cited by 3
Wang, X., Zhang, H., Zhou, W. P., Li, X., Ibáñez, C. F., & Xie, M. (2025) · BMC Biology · doi:10.1186/s12915-025-02230-x · Cited by 3
“A previous study reported that snRNA-seq has low sensitivity to detect microglial activation genes in human brains [56].”
Olayinka, O. A., O’Neill, N. K., Empawi, J. A., Bock, P., Hu, J., Rickner, H., Wong, M., Stein, T. D., Wolozin, B. L., Farrer, L. A., et al. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.107128 · Cited by 5
“Many of our findings were generated from snRNAseq data, and expression of some AD-associated microglial genes is depleted in single-nucleus RNAseq experiments compared to single-cell RNAseq experiments (Thrupp et al., 2020).”
Microglia modulate concussion biomarkers and cognitive recovery in male mice
Anderson, E. D., Augustin, D. V., Georges, A. P., Kim, T., Issadore, D., & Meaney, D. F. (2026) · Journal of Neuroinflammation · doi:10.1186/s12974-026-03999-6 · Cited by 1
In search of a Drosophila core cellular network with single-cell transcriptome data
Yang, M. Y., Harrison, B. R., & Promislow, D. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac212 · Cited by 2
Pei, G., Fernandes, B. S., Wang, Y., Manuel, A. M., Jia, P., & Zhao, Z. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.14.496100 · Cited by 2
“For instance, one recent study demonstrated that snRNA-seq was insufficient to 668 detect microglia activation genes in the human brain [60].”
BackgroundDeng, W., & Zhao, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.15.670239 · Cited by 2
“Meanwhile, in snRNA-seq experiments, cytoplasm content will be discarded during nuclei isolation 1 (Figure 1A), resulting in missingness of cytoplasm dynamic 1–4 .”
InfluentialCancer-myeloid cell invasive program in pediatric-type diffuse high-grade gliomaPreprint
Ruiz-Moreno, C., Collot, R. V. U., van den Broek, T. J. M., Wehrens, E. J., Bessler, N., Dharmadhikari, G., Pas, B. M. T., Ibarra, I. L., Metselaar, D. S., Kranendonk, M. E. G., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.23.701142 · Cited by 3
Distinct mechanistic pathways of early tauopathy revealed by MAPT mutationsPreprint
Foiani, M. S., Nirujogi, R., Watamura, N., Bez, S., Tsefou, E., Santambrogio, A., Patel, S., Hannah, D., Goulbourne, C. N., Fatima, N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.14.705716 · Cited by 2
“Nuclei isolation was performed as previously described 57 .”
Immunotherapy for Neurodegenerative Movement Disorders
Mosley, R. L., Saleh, M., & Olson, K. E. (2024) · Springer eBooks · doi:10.1007/978-3-031-68237-7_52 · Cited by 1
Role of progesterone action in inguinal hernia formation via skeletal muscle fibrosis and atrophy
You, T., Zandigohar, M., Potluri, T., Piehl, N., Coon, J. S., Baker, E., Kafali, M., Dai, Y. D., Stulberg, J. J., Escobar, D., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.193208 · Cited by 3
“This shortcoming, combined with the inherent inability of single-nuclei sequencing to capture cytoplasmic mRNA, limited our ability to capture the heterogeneity within these cell types and view differences in their gene expression between treatment groups (91–93).”
Multi-omic derived cell-type specific Alzheimer disease polygenic risk scores
O’Neill, N. K., Kurniansyah, N., Zhu, C., Olayinka, O. A., Mayeux, R. P., Haines, J. L., Pericak‐Vance, M. A., Wang, L.-S., Schellenberg, G. D., Farrer, L. A., et al. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.07.009 · Cited by 2
“Evidence suggests that MIC activation plays a pivotal role in AD (Gao et al., 2023; Leng and Edison, 2021), but technical limitations make it difficult to fully characterize the cellular subtype with snRNA-seq data (Thrupp et al., 2020).”
Lopes, K. D. P., Vialle, R. A., Green, G., Fujita, M., Gaiteri, C., Menon, V., Schneider, J. A., Wang, Y., Jager, P. L. D., Habib, N., et al. (2025) · Acta Neuropathologica Communications · doi:10.1186/s40478-025-02143-4 · Cited by 2
“The snRNASeq experiment captures the RNA from the nucleus, not capturing the cytoplasm RNA gene expression [58].”
Tomimatsu, M., Obana, M., Tanaka, S., Kajiura, R., Nagano, R., Matsumoto, K., Yokota, K., Kominami, S., Ito, T., Suzuki, S., et al. (2025) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00595.2025 · Cited by 1
A Single-Cell Omics Technical Guide for Advancing Neuropsychiatric Research
Casmey, K., Zimmermann, M., Xie, Y., Codeluppi-Arrowsmith, S. A., & Turecki, G. (2025) · Genes · doi:10.3390/genes16121394 · Cited by 3
“For example, the analysis of nuclei lacks information from cytoplasmic RNA, which can be important for the investigation of particular cell types, like for profiling microglial activation genes [97].”
Duc, P. M., Thủy, L. T. T., Hai, H., Bao, T. V., Ha, N. T., Anh, P. T., Ikenaga, H., Fujii, H., Yoshida, K., Enomoto, M., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.101783 · Cited by 5
Multiscale Approximations to Understand the Complex Role of Microglia in Alzheimer's Disease
Martínez-Tazo, P., Mejias‐Ortega, M., López-López, V., Bhojwani-Cabrera, A. M., Saxton, E., Canals, S., Hardingham, G. E., Gutiérrez, A., López‐Atalaya, J. P., & Santis, S. D. (2026) · European Journal of Neuroscience · doi:10.1111/ejn.70536 · Cited by 1
“…in post-mortem human tissue use single nuclei sequencing (snRNA-seq), a technique that, although more suitable for frozen samples, has lower transcriptional resolution than scRNA-seq, especially regarding the detection of genes associated with active cytoplasmic functions (Thrupp et al. 2020).”
Microglial state transitions are constrained by developmental and metabolic checkpoints
Mesaoudi, A. E., & Kim, D. W. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1852176 · Cited by 1
Neuroinflammation in glaucoma: a myriad of cellular pathways and players
MacLean, M., Lydon, S. D., Gomes, C., Pizzi, E. M., Diemler, C. A., Yablonski, S. E. R., Howell, G. R., Meyer, J. S., & Libby, R. T. (2026) · Mammalian Genome · doi:10.1007/s00335-026-10253-0 · Cited by 1
“It is plausible that this is due to the sparse representation of immune cells in the snRNA-seq dataset and snRNA-seq does not capture important microglial transcripts (Thrupp et al. 2020).”
Mu, C., Ding, Y., Weiss, A., Rosenfeld, S., Li, F., & Geng, X. (2026) · Biomolecules · doi:10.3390/biom16071054 · Cited by 1
“Its main limitation is that nuclear RNA represents only a fraction of the whole-cell transcriptome and contains a higher proportion of intronic and nascent transcripts [16,20].”
Transcriptomic Approaches to Cardiomyocyte–Biomaterial Interactions: A Review
Wen, Y., Yang, H., & Hong, Y. (2024) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.4c00303 · Cited by 1
Castro-Mendoza, P. B., Weaver, C. M., Chang, W., Medalla, M., Rockland, K. S., Lowery, L., McDonough, E. M., Varghese, M., Hof, P. R., Meyer, D. E., et al. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02819-y · Cited by 1
“…protein expression intensities in diverse morphological compartments beyond the nucleus and they are capable of much greater spatial resolution and capture efficiency since transcripts that are present at low levels cannot be detected with sNucSeq (Thrupp et al. 2020; Cardona-Alberich et al. 2021).”
Hes, C., Tomlinson, A. J., Michielsen, L., Murdoch, H. J., Soltani, F., Kokoeva, M. V., & Sabatini, P. V. (2025) · eLife · doi:10.1101/2024.09.19.613879 · Cited by 3
Prater, K. E., & Lin, K. (2024) · Glia · doi:10.1002/glia.24633 · Cited by 3
“There are studies which suggest that the transcriptional profile of single nuclei may differ from that of whole cells (Thrupp et al., 2020).”
Single-Cell RNA Sequencing (sc-RNAseq) in the Development of Biomedical Therapeutics
Saw, P. E., & Song, E. (2025) · Springer eBooks · doi:10.1007/978-981-96-3041-7_17 · Cited by 1
DeSana, A. J., Alfawares, Y., Khatri, R., Hopkins, T., Best, F. V., McGuire, J. L., & Ngwenya, L. B. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26189140 · Cited by 2
“While we chose to focus on the two microglial populations that we identified, it is necessary to point out that single-nucleus RNA sequencing may lack the sensitivity to observe all the microglial transcriptional states relevant to TBI-induced neuroinflammation [85].”
Patel, T., Carnwath, T., Wang, X., Allen, M., Lincoln, S., Lewis‐Tuffin, L. J., Quicksall, Z., Lin, S., Tutor‐New, F. Q., Ho, C. C. G., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-523348/v1 · Cited by 1
Takahashi, H., Bhagwagar, S. Z., Nies, S. H., Chiasseu, M., Wang, G., Mackenzie, I. R., & Strittmatter, S. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.25.521308 · Cited by 1
Astrocytic glutamate regulation is shaped by adversity and glucocorticoid signallingPreprint
Kaul, D., Curry, A. R., Gerstner, N., Fröhlich, A. S., Krontira, A. C., Kyrousi, C., Smith, C. C., Sutherland, G. T., Dottori, M., Ziller, M. J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.24.600362 · Cited by 3
Hes, C., Tomlinson, A. J., Michielsen, L., Murdoch, H. J., Soltani, F., Kokoeva, M. V., & Sabatini, P. V. (2025) · eLife · doi:10.7554/elife.106217.1 · Cited by 1
Age-associated microglia populations identified from several single cell transcriptome dataPreprint
Sugai, A., Moridono, H., Bilgic, M., Gotoh, Y., & Kishi, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.28.665226 · Cited by 1
Revanna, J. S., Wessendorf-Rodriguez, K. A., Xiao, Q., Sabedot, T. S., Cuoco, M. S., Sarkar, S., Zhou-Yang, L., Lim, C. K., Prozapas, V. N., Wooldridge, R., et al. (2026) · SSRN Electronic Journal · doi:10.64898/2026.05.12.724612 · Cited by 2
“Additionally, ROSMAP is based on single-nucleus RNA-seq, whereas our data are derived from whole cells; prior studies have shown that single-nucleus approaches can underrepresent key microglial activation genes, including APOE, SPP1, and CD74 91 .”
Back to basics: Transcriptomics studies for deep phenotyping of osteoarthritis
Rai, M. F. (2021) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2021.100166
杨榕 (2012) · 福建艺术
Xu, M., Wang, S., Jiang, Y., Wang, J., Xiong, Y., Dong, W., Yao, Q., Xing, Y., Yu, D., Liu, F., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4022083
Patel, T., Carnwath, T., Wang, X., Allen, M., Lincoln, S., Lewis‐Tuffin, L. J., Quicksall, Z., Lin, S., Tutor‐New, F. Q., Ho, C. C. G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.13.444000
Cell-associated Transcriptional Alterations in the Retinal of Alzheimer’s DiseasePreprint
Ngolab, J., Mark, A., Buchanan, J., Korouri, S., Priessl, S., Rosenthal, S. B., Wang, A., Fisch, K. M., & Rissman, R. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.18.502974
Yeung, K., Raja, K. K. B., Shim, Y.-K., Li, Y., Chen, R., & Mardon, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.18.512740
Lemcke, R., Egebjerg, C., Berendtsen, N. T., Egerod, K. L., Thomsen, A. R., Pers, T. H., Christensen, J. P., & Kornum, B. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.06.530999
“been reported to be challenging, due to a depletion of certain marker genes in the nucleus especially in human tissue (Thrupp et al., 2020).”
Martins-Ferreira, R., Calafell-Segura, J., Chaves, J., Ciudad, L., da Silva, A. M., Costa, P., Leal, B., & Ballestar, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.21.545837
Martins-Ferreira, R., Calafell-Segura, J., Chaves, J., Ciudad, L., da Silva, A. M., Costa, P., Leal, B., & Ballestar, E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4512951
Lemcke, R., Egebjerg, C., Berendtsen, N. T., Egerod, K. L., Thomsen, A. R., Pers, T. H., Christensen, J. P., & Kornum, B. R. (2023) · eLife · doi:10.7554/elife.87515.2
Chen, S., Chang, Y., Li, L., Acosta, D., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601026
Chen, S., Chang, Y., Li, L., Acosta, D. M., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601020
Zhang, Y., Tan, J., Yang, K., Fan, W., Yu, B., & Shi, W. (2023) · Figshare · doi:10.6084/m9.figshare.23583532
Chen, S., Chang, Y., Li, L., Acosta, D. M., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601017
Chen, S., Chang, Y., Li, L., Acosta, D., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601011
Chen, S., Chang, Y., Li, L., Acosta, D. M., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601014
Chen, S., Chang, Y., Li, L., Acosta, D. M., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601032
Chen, S., Chang, Y., Li, L., Acosta, D., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601023
Chen, S., Chang, Y., Li, L., Acosta, D. M., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C. T., Julian, D. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22601029
The liver sinusoid at single-cell level
Parhar, R., & Ramachandran, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95262-0.00006-1
Multi region dissection of Alzheimer's brain at single cell level
Mao, M., Wang, C., Ma, X., & Ye, J. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.12.010
Evaluating gene representation in spatial transcriptomics across pre-designed panelsPreprint
Seo, H., & Krawetz, R. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.07.647642
“While ST platforms achieve near-or sub-cellular resolution, they often suffer from low transcript detection efficiency, making it difficult to capture rare or low-abundance transcripts 4,5 .”
Hes, C., Tomlinson, A. J., Michielsen, L., Murdoch, H. J., Soltani, F., Kokoeva, M. V., & Sabatini, P. V. (2025) · eLife · doi:10.7554/elife.106217.2
Dostalova, D., Abaffy, P., Rohlová, E., Kriška, J., Knotek, T., Turečková, J., Kirdajová, D., Anděrová, M., & Valihrach, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.11.669607
“Additionally, recent work has shown that snRNA-seq underrepresents many transcripts associated with microglial activation, including key immune and disease-related genes, calling into question its sensitivity for capturing cellular responses in pathology (2).”
Metadata
- Authors
- Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., de Strooper, B., & Fiers, M.
- Affiliations
- Vlaams Instituut voor Biotechnologie (BE)
- UK Dementia Research Institute (GB)
- KU Leuven (BE)
- University of London (GB)
- Corresponding author
- Fiers, Mark
- ORCID
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 32
- Issue
- 13
- Pages
- 108189
- ISSN
- 2211-1247 (ISSN-L)
- PMC
- PMC7527779
- OpenAlex
- W3091491962
- Cited by
- 329
- References
- 50
- Citations by year
- 2026: 40 · 2025: 70 · 2024: 48 · 2023: 57 · 2022: 54 · 2021: 57 · 2020: 2
- FWCI
- 12.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Roche
- KU Leuven
- Biogen
- Vlaams Instituut voor Biotechnologie
- European Resuscitation Council (834682)
- Alzheimer's Association
- Vlaamse regering
- Alzheimer Society
- NIHR Imperial Biomedical Research Centre
- European Commission
- Novartis
- UK Dementia Research Institute (FWO 1830717N)
- GlaxoSmithKline
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- single-nucleus RNA sequencing, microglial activation, human microglia, apolipoprotein E, CST3, osteopontin, CD74, Alzheimer's disease
- MeSH terms
- Humans, Sequence Analysis, RNA, Microglia, Gene Expression Profiling, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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