The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
CoolMPS for robust sequencing of single-nuclear RNAs captured by droplet-based method
Abstract
Massively-parallel single-cell and single-nucleus RNA sequencing (scRNA-seq, snRNA-seq) requires extensive sequencing to achieve proper per-cell coverage, making sequencing resources and availability of sequencers critical factors for conducting deep transcriptional profiling. CoolMPS is a novel sequencing-by-synthesis approach that relies on nucleotide labeling by re-usable antibodies, but whether it is applicable to snRNA-seq has not been tested. Here, we use a low-cost and off-the-shelf protocol to chemically convert libraries generated with the widely-used Chromium 10X technology to be sequenceable with CoolMPS technology. To assess the quality and performance of converted libraries sequenced with CoolMPS, we generated a snRNA-seq dataset from the hippocampus of young and old mice. Native libraries were sequenced on an Illumina Novaseq and libraries that were converted to be compatible with CoolMPS were sequenced on a DNBSEQ-400RS. CoolMPS-derived data faithfully replicated key characteristics of the native library dataset, including correct estimation of ambient RNA-contamination, detection of captured cells, cell clustering results, spatial marker gene expression, inter- and intra-replicate differences and gene expression changes during aging. In conclusion, our results show that CoolMPS provides a viable alternative to standard sequencing of RNA from droplet-based libraries.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures




Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“A detailed description and comparison of both approaches for different RNA sequencing protocols (48,49) yielded slight advantages for the latter sequencing approach in small RNA sequencing without affecting the general expression patterns.”
From miRNATissueAtlas2: an update to the human miRNA tissue atlas (2022) · cites this paper as [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 2,842
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794
Opportunities and challenges in long-read sequencing data analysis
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619 · cited 4×
“We detected that this contamination––sometimes referred to as ’soup’ (25)––varied among samples but was not statistically different across library types (Supplementary Figure S6e).”
BackgroundInfluentialThe sequence of sequencers: The history of sequencing DNA
Heather, J. M., & Chain, B. (2016) · Genomics · doi:10.1016/j.ygeno.2015.11.003 · Cited by 1,397
A single-cell transcriptomic atlas characterizes ageing tissues in the mouse
The Tabula Muris Consortium, Almanzar, N., Antony, J., Baghel, A. S., Bakerman, I., Bansal, I., Barres, B. A., Beachy, P. A., Berdnik, D., Bilen, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2496-1 · Cited by 1,247
Self-citationConstruction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087
Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice
Villeda, S. A., Plambeck, K. E., Middeldorp, J., Castellano, J. M., Mosher, K. I., Luo, J., Smith, L. K., Bieri, G., Lin, K., Berdnik, D., et al. (2014) · Nature Medicine · doi:10.1038/nm.3569 · Cited by 1,081
Self-citationMassively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069
Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease
Gate, D., Saligrama, N., Leventhal, O., Yang, A. C., Unger, M. S., Middeldorp, J., Chen, K., Lehallier, B., Channappa, D., De Los Santos, M. B., et al. (2020) · Nature · doi:10.1038/s41586-019-1895-7 · Cited by 992
Self-citationOrchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Ageing hallmarks exhibit organ-specific temporal signatures
Schaum, N., Lehallier, B., Hahn, O., Pálovics, R., Hosseinzadeh, S., Lee, S. E., Sit, R., Lee, D. P., Losada, P. M., Zardeneta, M. E., et al. (2020) · Nature · doi:10.1038/s41586-020-2499-y · Cited by 671
Self-citationDiv-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601
Single-cell analysis reveals T cell infiltration in old neurogenic niches
Dulken, B. W., Buckley, M. T., Navarro Negredo, P., Saligrama, N., Cayrol, R., Leeman, D. S., George, B. M., Boutet, S. C., Hebestreit, K., Pluvinage, J. V., et al. (2019) · Nature · doi:10.1038/s41586-019-1362-5 · Cited by 521
Self-citationCaloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging
Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.008 · Cited by 349
Systemic factors as mediators of brain homeostasis, ageing and neurodegeneration
Pluvinage, J. V., & Wyss-Coray, T. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0255-9 · Cited by 191
Self-citationMurine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
Kimmel, J. C., Penland, L., Rubinstein, N. D., Hendrickson, D. G., Kelley, D. R., & Rosenthal, A. Z. (2019) · Genome Research · doi:10.1101/gr.253880.119 · Cited by 184 · cited 1×
“For this study, we chose to assess the performance of converted libraries generated with the Chromium Single-cell ‘3 reagent kit (version 3), as it is currently among the most widely used droplet-based technologies, and several singlecell and single-nucleus atlas datasets have been generated with it (7,9,11,17,39,40).”
Method
Research works printed but not linked: 23
- Levy, 2016, Advancements in next-generation sequencing, Annu. Rev. Genomics Hum. Genet., 17, 95, 10.1146/annurev-genom-083115-022413 · DOI
- Wang, 2020, Changing technologies of RNA sequencing and their applications in clinical oncology, Front. Oncol., 10, 447, 10.3389/fonc.2020.00447 · DOI
- Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat. Commun., 8, 14049, 10.1038/ncomms14049 · DOI · Paper page
- Mathys, 2019, Single-cell transcriptomic analysis of Alzheimer's disease, Nature, 570, 332, 10.1038/s41586-019-1195-2 · DOI · Paper page
- Natarajan, 2019, Comparative analysis of sequencing technologies for single-cell transcriptomics, Genome Biol, 20, 70, 10.1186/s13059-019-1676-5 · DOI
- Senabouth, 2020, Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing, NAR Genom Bioinform, 2, lqaa034, 10.1093/nargab/lqaa034 · DOI
- Ding, 2020, Systematic comparison of single-cell and single-nucleus RNA-sequencing methods, Nat. Biotechnol., 38, 737, 10.1038/s41587-020-0465-8 · DOI · Paper page
- Keren-Shaul, 2017, A unique microglia type associated with restricting development of Alzheimer's disease, Cell, 169, 1276, 10.1016/j.cell.2017.05.018 · DOI · Paper page
- Zhou, 2020, Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease, Nat. Med., 26, 131, 10.1038/s41591-019-0695-9 · DOI · Paper page
- Habib, 2020, Disease-associated astrocytes in Alzheimer's disease and aging, Nat. Neurosci., 23, 701, 10.1038/s41593-020-0624-8 · DOI · Paper page
- Ximerakis, 2019, Single-cell transcriptomic profiling of the aging mouse brain, Nat. Neurosci., 22, 1696, 10.1038/s41593-019-0491-3 · DOI · Paper page
- Cembrowski, 2016, Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons, Elife, 5, e14997, 10.7554/eLife.14997 · DOI
- Parekh, 2018, zUMIs - a fast and flexible pipeline to process RNA sequencing data with UMIs, Gigascience, 7, giy059, 10.1093/gigascience/giy059 · DOI
- Crowell, 2019, On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data, 10.1101/713412 · DOI
- Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol, 15, 550, 10.1186/s13059-014-0550-8 · DOI · Paper page
- Stuart, 2019, Comprehensive Integration of Single-Cell Data, Cell, 177, 1888, 10.1016/j.cell.2019.05.031 · DOI · Paper page
- Lun, 2019, EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data, Genome Biol., 20, 63, 10.1186/s13059-019-1662-y · DOI
- Wegiel, 2002, Vascular fibrosis and calcification in the hippocampus in aging, Alzheimer disease, and Down syndrome, Acta Neuropathol, 103, 333, 10.1007/s00401-001-0471-y · DOI
- Stankiewicz, 2015, The effect of acute and chronic social stress on the hippocampal transcriptome in mice, PLoS One, 10, e0142195, 10.1371/journal.pone.0142195 · DOI
- Kitraki, 1993, Aging-related changes in IGF-II and c-fos gene expression in the rat brain, Int. J. Dev. Neurosci., 11, 1, 10.1016/0736-5748(93)90029-D · DOI
- Schaum, 2019, Ageing hallmarks exhibit organ-specific temporal signatures, Nature, 583, 596, 10.1038/s41586-020-2499-y · DOI · Paper page
- 1000 Genomes Project Consortium, 2015, A global reference for human genetic variation, Nature, 526, 68, 10.1038/nature15393 · DOI · Paper page
- Chhangawala, 2015, The impact of read length on quantification of differentially expressed genes and splice junction detection, Genome Biol., 16, 131, 10.1186/s13059-015-0697-y · DOI
Cited by
Sorted by citations
Atlas of the aging mouse brain reveals white matter as vulnerable foci
Hahn, O., Foltz, A. G., Atkins, M., Kedir, B., Moran-Losada, P., Guldner, I. H., Munson, C., Kern, F., Pálovics, R., Lu, N., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.027 · Cited by 213
Self-citationmiRNATissueAtlas2: an update to the human miRNA tissue atlas
Keller, A., Gröger, L., Tschernig, T., Solomon, J., Laham, O., Schaum, N., Wagner, V., Kern, F., Schmartz, G. P., Li, Y., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab808 · Cited by 143
Self-citationTsai, A. P., Dong, C., Lin, P. B.-C., Oblak, A. L., Viana Di Prisco, G., Wang, N., Hajicek, N., Carr, A. J., Lendy, E. K., Hahn, O., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.08.008 · Cited by 71
Self-citation
Metadata
- Authors
- Hahn, O., Fehlmann, T., Zhang, H., Munson, C. N., Vest, R. T., Borcherding, A., Liu, S., Villarosa, C., Drmanac, S., Drmanac, R., Keller, A., & Wyss-Coray, T.
- Affiliations
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
- MGI, 2904 Orchard Pkwy, San Jose, CA, USA
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12-02
- Volume
- 49
- Issue
- 2
- Pages
- e11-e11
- ISSN
- 0305-1048
- 1362-4962
- PMC
- PMC7826285
- Cited by
- 16
- References
- 17
- Language
- en
- Funders
- Nan Fung Life Sciences
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review