Semi-automated annotation refinement accelerates cell type identification in brain spatial and single-cell studies
Open accessAbstract
Background: single-cell and spatial omic techniques have enabled the investigation of cell type specific alterations in biologically complex tissues. In an effort to map cell taxonomies, large atlas-based studies and multi-laboratory consortia have created sets of annotated cell types. However, application of atlas- or database-level knowledge to individual studies is often resource-limited and computational demands scale with the size of both query and reference datasets. Results: Here, we report a statistical framework for rapid label transfer using summary statistics and user-defined hyperparameters. Semi-Automated Hand Annotation (SAHA) 1 allows the user to investigate magnitude, directionality, and statistical significance of matches between unnamed query clusters and reference cell types using either marker-based or marker-free methodologies. By pre-loading the package with summary statistics from the Allen Brain Cell Atlas of the mouse brain, the SAHA R package is capable of rapid cell type comparisons that closely mimic cell typing by integration-based annotation strategies. Furthermore, this flexible package is capable of comparisons across omic modalities, cluster resolutions, and annotations from any study where summary statistics are available. We demonstrate this flexibility by using multiple single-nuclei studies of the mouse cerebellum, mouse cerebral cortex, human cerebral cortex, human peripheral blood mononuclear cells, and one mouse spatial transcriptomic assay. Importantly, this method avoids privacy concerns as it does not require the sharing or deposition of raw data in a web-based tool. Conclusions: As a result, SAHA offers a non-deterministic annotation reporting structure with automated html reports and summary statistics for transparency in cell typing decisions. Taken together, this scalable framework implemented as a package in R affords increased biological insight into the annotation of single-cell and spatial datasets. SHORT SUMMARY: Acri and colleagues present rapid cell type annotation without the need for dataset integration. This paper outlines the utility of the package, SAHA, in annotating neurological datasets.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Cites
Correlation Coefficients: Appropriate Use and Interpretation
Schober, P., Boer, C., & Schwarte, L. A. (2018) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002864 · Cited by 10,986 · cited 1× · see citing works
“SAHA implements a Fisher’s exact test 60 for per-cluster markers and a correlation 61 of scaled, normalized average expression profiles to aid in annotation.”
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 3,326 · see citing works
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · cited 1× · see citing works
“The marker-based strategy closely resembles traditional hand annotation implemented since the commercialization of single-cell assays 62,63 .”
Microglia states and nomenclature: A field at its crossroads
Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973 · cited 1× · see citing works
“There is a growing body of evidence that certain cell types take on contextual cell states, especially glial cell types during neurodegenerative diseases 37 .”
Hallmarks of neurodegenerative diseases
Wilson, D. M., Cookson, M. R., Van Den Bosch, L., Zetterberg, H., Holtzman, D. M., & Dewachter, I. (2023) · Cell · doi:10.1016/j.cell.2022.12.032 · Cited by 1,789 · cited 2× · see citing works
“This challenge is especially critical in complex biological tissues such as the central nervous system, where certain neuronal subtypes have been shown to be selectively vulnerable to neurodegenerative diseases 8 and glial substates are implicated in critical immune responses to pathology 9 .”
Self-citationPanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data
Franzén, O., Gan, L.-M., & Björkegren, J. L. M. (2019) · Database · doi:10.1093/database/baz046 · Cited by 1,551 · cited 3× · see citing works
“…the two clusters which were depleted in a mouse model of amyloidosis 35 with the deletion of an E3 ubiquitin ligase (IDOL) (clusters 10 and 22) were both annotated as Somatostatin positive chondrolectin expressing inhibitory neurons (056 Sst Chodl Gaba), a distinct subtype of neurons…”
Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration
Deczkowska, A., Keren-Shaul, H., Weiner, A., Colonna, M., Schwartz, M., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2018.05.003 · Cited by 1,474 · cited 1× · see citing works
“This challenge is especially critical in complex biological tissues such as the central nervous system, where certain neuronal subtypes have been shown to be selectively vulnerable to neurodegenerative diseases 8 and glial substates are implicated in critical immune responses to pathology 9 .”
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Hu, C., Li, T., Xu, Y., Zhang, X., Li, F., Bai, J., Chen, J., Jiang, W., Yang, K., Ou, Q., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac947 · Cited by 1,284 · cited 1× · see citing works
“Current solutions include the creation of consensus atlases 14–20 and brute-force databases 21,22 containing all possible cell type designations and markers-of-interest.”
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · cited 1× · see citing works
“Although no gold standard exists for determining power or standard usage of single-cell data, innovative new technologies are beginning to enable integration of epigenetic and spatial data 52 .”
A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,194 · see citing works
Clustering trees: a visualization for evaluating clusterings at multiple resolutions
Zappia, L., & Oshlack, A. (2018) · GigaScience · doi:10.1093/gigascience/giy083 · Cited by 1,064 · see citing works
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056 · cited 2× · see citing works
“Current solutions include the creation of consensus atlases 14–20 and brute-force databases 21,22 containing all possible cell type designations and markers-of-interest.”
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · cited 1× · see citing works
“The marker-based strategy closely resembles traditional hand annotation implemented since the commercialization of single-cell assays 62,63 .”
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · cited 1× · see citing works
“Technologies focused on assaying transcriptomes, proteomes, and epigenomes with single-cell precision or spatial resolution have increased the granularity and dimensionality with which cell types can be classified 2–4 .”
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · cited 2× · see citing works
“…reference inputs in this study: all cell types from multiple regions of the mouse brain 20 , human glial substates derived from integrative analysis of 4 multi-region neurodegeneration single nuclei atlases 29 , and PBMCs from individuals infected with influenza, COVID-19, or healthy controls 30 .”
Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722 · cited 1× · see citing works
“However, the gene which encodes IBA1, AIF1, does not necessarily translate into a reliable transcriptomic marker of microglia in single-nuclei RNA sequencing (snRNA-seq) studies 12 .”
UPTON, G. J. G. (1992) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982890 · Cited by 636 · see citing works
Selective vulnerability in neurodegenerative diseases
Fu, H., Hardy, J., & Duff, K. E. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0221-2 · Cited by 585 · cited 1× · see citing works
“Neuronal diversity may be defined by classes varying in neurotransmitter release 47 , circuit and layer projection 48 , or susceptibility to disease 49 .”
Marsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 432 · see citing works
Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works
Fibrous and protoplasmic astrocytes are biochemically and developmentally distinct
Miller, R., & RAFF, M. C. (1984) · Journal of Neuroscience · doi:10.1523/jneurosci.04-02-00585.1984 · Cited by 396 · cited 1× · see citing works
“While less is known regarding astrocyte states in response to disease, the major divide between protoplasmic-and fibrous-states is well described 39 .”
A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types
Kozareva, V., Martin, C., Osorno, T., Rudolph, S., Guo, C., Vanderburg, C. R., Nadaf, N. M., Regev, A., Regehr, W. G., & Macosko, E. Z. (2021) · Nature · doi:10.1038/s41586-021-03220-z · Cited by 370 · cited 5× · see citing works
“…datasets from five sources were used to create query inputs from this study: a mouse cerebellum single-cell study published prior to the ABC atlas 32 , a mouse snRNAseq dataset profiling the isocortex without celltype enrichment or sorting 35 , spatial transcriptomic data available from Vizgen…”
InfluentialSingle-cell multiregion dissection of Alzheimer’s disease
Mathys, H., Boix, C. A., Akay, L. A., Xia, Z., Davila-Velderrain, J., Ng, A. P., Jiang, X., Abdelhady, G., Galani, K., Mantero, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07606-7 · Cited by 343 · cited 4× · see citing works
“These studies have enabled discovery of subtype-specific alterations in studies of neurodegenerative diseases 23,44–46 , schizophrenia 53 , and other neurological disorders 54 .”
InfluentialA blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328 · cited 1× · see citing works
“As this comparison required a new, non-brain reference dataset, we selected PBMCs from healthy donors in the Covid-19 Multi-omics Blood Atlas 34 (COMBAT project; 92,205 annotated cells from 124 non-disease donors).”
A single-cell atlas of the normal and malformed human brain vasculature
Winkler, E. A., Kim, C. N., Ross, J. M., Garcia, J. H., Gil, E., Oh, I., Chen, L. Q., Wu, D., Catapano, J. S., Raygor, K. P., et al. (2022) · Science · doi:10.1126/science.abi7377 · Cited by 318 · see citing works
Automatic cell-type harmonization and integration across Human Cell Atlas datasets
Xu, C., Prete, M., Webb, S., Jardine, L., Stewart, B. J., Hoo, R., He, P., Meyer, K. B., & Teichmann, S. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.026 · Cited by 289 · see citing works
Transformers in single-cell omics: a review and new perspectives
Szałata, A., Hrovatin, K., Becker, S., Tejada-Lapuerta, A., Cui, H., Wang, B., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02353-z · Cited by 176 · cited 1× · see citing works
“Technologies focused on assaying transcriptomes, proteomes, and epigenomes with single-cell precision or spatial resolution have increased the granularity and dimensionality with which cell types can be classified 2–4 .”
Dolan, M.-J., Therrien, M., Jereb, S., Kamath, T., Gazestani, V., Atkeson, T., Marsh, S. E., Goeva, A., Lojek, N. M., Murphy, S., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01558-2 · Cited by 174 · see citing works
Single-cell genomics and regulatory networks for 388 human brains
Emani, P. S., Liu, J. J., Clarke, D., Jensen, M., Warrell, J., Gupta, C., Meng, R., Lee, C.-Y., Xu, S., Dursun, C., et al. (2024) · Science · doi:10.1126/science.adi5199 · Cited by 168 · see citing works
Single-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka, W. B., Mohammadi, S., Fullard, J. F., Dávila-Velderrain, J., Subburaju, S., Tso, D. R., Hourihan, M., Jiang, S., Lee, H.-C., Bendl, J. M., et al. (2024) · Science · doi:10.1126/science.adg5136 · Cited by 155 · cited 1× · see citing works
“These studies have enabled discovery of subtype-specific alterations in studies of neurodegenerative diseases 23,44–46 , schizophrenia 53 , and other neurological disorders 54 .”
Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD
Pineda, S. S., Lee, H., Fitzwalter, B. E., Mohammadi, S., Pregent, L., Gardashli, M., Mantero, J. C., Engelberg-Cook, E., DeJesus‐Hernandez, M., van Blitterswijk, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.02.031 · Cited by 150 · cited 3× · see citing works
“These studies have enabled discovery of subtype-specific alterations in studies of neurodegenerative diseases 23,44–46 , schizophrenia 53 , and other neurological disorders 54 .”
Multi-omic profiling of the developing human cerebral cortex at the single-cell level
Zhu, K., Bendl, J. M., Rahman, S., Vicari, J. M., Coleman, C., Clarence, T., Latouche, O., Tsankova, N. M., Li, A., Brennand, K., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg3754 · Cited by 116 · cited 1× · see citing works
“In particular, datasets from the mammalian brain are highly heterogeneous and contain transient, context-specific cell states across development 40 , non-pathological aging 41–43 , and disease 23,29,44–46 .”
Wang, D. (2008) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2008.07.014 · Cited by 106 · cited 1× · see citing works
“Whether this discrepancy is due to the known discordance between proteomic and transcriptomic assays 13 or a true divergence between protein-and RNA-markers of cell identity is unknown.”
Schwab, J., Frei, E., Klusman, I., Schnell, L., Schwab, M., & Schluesener, H. (2001) · Journal of Neuroimmunology · doi:10.1016/s0165-5728(01)00375-7 · Cited by 102 · cited 1× · see citing works
“For example, microglia marker IBA1 is one of the most commonly used protein markers in histological staining 11 .”
Establishing a conceptual framework for holistic cell states and state transitions
Rafelski, S. M., & Theriot, J. A. (2024) · Cell · doi:10.1016/j.cell.2024.04.035 · Cited by 92 · cited 2× · see citing works
“One challenge in identifying these states is that they are transient and may not contribute sufficient variability to form discrete clusters 28 .”
Gerrits, E., Giannini, L., Brouwer, N., Melhem, S., Seilhean, D., Ber, I. L., The Brainbank Neuro-CEB Neuropathology Network, Kamermans, A., Kooij, G., de Vries, H. E., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01124-3 · Cited by 82 · see citing works
Lineage origins of GABAergic versus glutamatergic neurons in the neocortex
Marín, Ó., & Müller, U. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.015 · Cited by 80 · cited 1× · see citing works
“Neuronal diversity may be defined by classes varying in neurotransmitter release 47 , circuit and layer projection 48 , or susceptibility to disease 49 .”
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · cited 1× · see citing works
“Even with the advent of integration-based label transfer 24 , cell cluster determination is still sensitive to study-specific artifacts and annotation disagreements.”
Single-cell spatial transcriptomic atlas of the whole mouse brain
Han, L., Liu, Z., Jing, Z., Liu, Y., Chang, H., Lei, J., Peng, Y., Wang, K., Xu, Y., Liu, W., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.02.015 · Cited by 70 · see citing works
Discrepant mRNA and Protein Expression in Immune Cells
Li, J., Zhang, Y., Yang, C., & Rong, R. (2020) · Current Genomics · doi:10.2174/1389202921999200716103758 · Cited by 60 · cited 2× · see citing works
“…annotations, the two clusters which were depleted in a mouse model of amyloidosis 35 with the deletion of an E3 ubiquitin ligase (IDOL) (clusters 10 and 22) were both annotated as Somatostatin positive chondrolectin expressing inhibitory neurons (056 Sst Chodl Gaba), a distinct subtype of…”
Single-cell genomics and regulatory networks for 388 human brainsPreprint
Emani, P. S., Liu, J. J., Clarke, D., et al. (2024) · bioRxiv · doi:10.1101/2024.03.18.585576 · Cited by 59 · cited 1× · see citing works
“These studies have enabled discovery of subtype-specific alterations in studies of neurodegenerative diseases 23,44–46 , schizophrenia 53 , and other neurological disorders 54 .”
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · cited 1× · see citing works
“Historically, cell type identity has been defined through a combination of histological staining 5 , morphological analysis 6 , and marker profiling 7 .”
Nano, P. R., Fazzari, E., Azizad, D. J., Martija, A. A., Nguyen, C. V., Wang, S., Giang, V., Kan, R. L., Yoo, J., Wick, B. D., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01933-2 · Cited by 39 · cited 1× · see citing works
“Secondary meta-analysis has enabled some cross-study comparisons 29,55 , however the largest challenge of determining a taxonomy for a single-cell dataset is inherently a problem of cluster annotation.”
scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data
Ji, X., Tsao, D., Bai, K., Tsao, M., Xing, L., & Zhang, X. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad030 · Cited by 36 · cited 1× · see citing works
“Several command-line 56,57 and web tools 58,59 are available to perform integration-based clustering, however the memory and bandwidth constraints remain major limiting factors.”
Fisher, J., Verhagen, M. G., Long, Z., Moissidis, M., Yan, Y., He, C., Wang, J., Micoli, E., Alastruey, C. M., Moors, R., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.11.013 · Cited by 36 · cited 1× · see citing works
“…35 with the deletion of an E3 ubiquitin ligase (IDOL) (clusters 10 and 22) were both annotated as Somatostatin positive chondrolectin expressing inhibitory neurons (056 Sst Chodl Gaba), a distinct subtype of neurons known to project from the cortex to the hippocampus and hypothalamus 64 .”
Mesecar, M. E., Duffy, M. F., Acri, D. J., Ding, J., Langston, R. G., Shah, S. I., Nalls, M. A., Reed, X., Scholz, S. W., Whitaker, D. T., et al. (2026) · npj Aging · doi:10.1038/s41514-026-00391-9 · Cited by 26 · see citing works
Self-citationSpatial and single-cell transcriptomics reveal the reorganization of cerebellar microglia with aging
Tsai, A. P., Henze, D. E., Lopez, E. R., Haberberger, J., Dong, C., Lu, N., Atkins, M., Costa, E. K., Farinas, A., Oh, H. S., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116624 · Cited by 22 · cited 1× · see citing works
“On the other hand, glial diversity is most commonly associated with regional heterogeneity 50 or polarization during stimuli 51 .”
Sex effects on gene expression across the human cerebral cortex at cell type resolution
DeCasien, A. R., Auluck, P. K., Liu, S., Feng, N., Elkahloun, A. G., Xu, Q., Marenco, S., Cookson, M., & Raznahan, A. (2026) · Science · doi:10.1126/science.aea9063 · Cited by 21 · see citing works
Self-citationLee, D., Koutrouli, M., Masse, N. Y., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz70855_097915 · Cited by 20 · see citing works
Schwarz, J. M. (2015) · Journal of Visualized Experiments · doi:10.3791/52537 · Cited by 18 · cited 1× · see citing works
“Historically, cell type identity has been defined through a combination of histological staining 5 , morphological analysis 6 , and marker profiling 7 .”
COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas
Wang, D., Kumar, V., Burnham, K. L., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1019 · Cited by 16 · cited 3× · see citing works
“…reference inputs in this study: all cell types from multiple regions of the mouse brain 20 , human glial substates derived from integrative analysis of 4 multi-region neurodegeneration single nuclei atlases 29 , and PBMCs from individuals infected with influenza, COVID-19, or healthy controls 30 .”
A multi-region single nucleus transcriptomic atlas of Parkinson’s disease
Prashant, N. M., Fullard, J. F., Clarence, T., Mathur, D. D., Casey, C., Hennigan, E., Alvia, M., Krause-Massaguer, J., Barreda, A., Davis, D. A., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-04117-y · Cited by 15 · cited 3× · see citing works
“These studies have enabled discovery of subtype-specific alterations in studies of neurodegenerative diseases 23,44–46 , schizophrenia 53 , and other neurological disorders 54 .”
MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomiesPreprint
Daniel, S. F., Lee, C., Mollenkopf, T. S., Lee, M. H., Arbuckle, J., Fiabane, E., Gabitto, M. I., Johansen, N., Kapen, I., Kraft, A. W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.06.710160 · Cited by 13 · cited 1× · see citing works
“Several command-line 56,57 and web tools 58,59 are available to perform integration-based clustering, however the memory and bandwidth constraints remain major limiting factors.”
Neuronal Circuit Evolution: From Development to Structure and Adaptive SignificancePreprint
Konstantinides, N., & Desplan, C. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041493 · Cited by 12 · cited 1× · see citing works
“Neuronal diversity may be defined by classes varying in neurotransmitter release 47 , circuit and layer projection 48 , or susceptibility to disease 49 .”
Wu, C.-H., Zhou, X., & Chen, M. (2025) · Genome biology · doi:10.1186/s13059-025-03525-6 · Cited by 10 · cited 1× · see citing works
“Other artifacts such as sample preparation or donor-specific variability pose similar clustering challenges 26,27 .”
Fishman, R. S. (2007) · Archives of Ophthalmology · doi:10.1001/archopht.125.5.690 · Cited by 9 · cited 1× · see citing works
“Historically, cell type identity has been defined through a combination of histological staining 5 , morphological analysis 6 , and marker profiling 7 .”
Asenjo-Martínez, A., Dragičević, K., Hou, W., Ozsvár, A., Hansen, N. W., Pfisterer, U., Rydbirk, R., Demharter, S., Møller, B. E., Gasthaus, J., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.028 · Cited by 9 · cited 1× · see citing works
“Interestingly, the Sst_Chodl subtype comprises long-range GABAergic projecting neurons that mature late in development and have been shown to also be selectively vulnerable to neuropsychiatric risk associated with sleep disturbances 65 .”
Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
Kersey, H. N., Acri, D. J., Dabin, L. C., Hartigan, K. A., Mustaklem, R., Park, J. H., & Kim, J. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101337 · Cited by 8 · see citing works
Self-citationCatching, A., Weller, C. A., Hu, F., Bromberek, S., Abbas, S., Daida, K., Malik, L., Baker, B., Auluck, P. K., Screven, L. A., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117110 · Cited by 4 · cited 1× · see citing works
“In particular, datasets from the mammalian brain are highly heterogeneous and contain transient, context-specific cell states across development 40 , non-pathological aging 41–43 , and disease 23,29,44–46 .”
Self-citationIntegrative analysis reveals generalizable human neurodegenerative disease-associated glial statesPreprint
Horan-Portelance, L., Acri, D. J., Mann, A., Brooks, J., Bailey, H. M., Gibbs, J. R., DeCasien, A. R., & Cookson, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.25.678630 · Cited by 3 · cited 6× · see citing works
“Public datasets were accessed for two purposes: i) use as query samples to attempt annotations 32,33,35,36,38 and ii) to use as database references 20,29,30 for label transfer via a summary statistic-based, non-integration method.”
InfluentialSelf-citationChi, Y., Marini, S., & Wang, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.038 · Cited by 2 · cited 1× · see citing works
“Several command-line 56,57 and web tools 58,59 are available to perform integration-based clustering, however the memory and bandwidth constraints remain major limiting factors.”
Karahan, H., Hartigan, K., Al‐Amin, M. M., John, S. K., McCord, B., Wijeratne, H. R. S., Acri, D. J., Smith, D. C., Dabin, L. C., Cordero, H. M. R., et al. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70949 · Cited by 2 · cited 4× · see citing works
“With the addition of ABC annotations, the two clusters which were depleted in a mouse model of amyloidosis 35 with the deletion of an E3 ubiquitin ligase (IDOL) (clusters 10 and 22) were both annotated as Somatostatin positive chondrolectin expressing inhibitory neurons (056 Sst Chodl Gaba), a…”
InfluentialSelf-citationAcri, D. J., Mesecar, M. E., Horan-Portelance, L., et al. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.14040698 · Cited by 1 · see citing works
Self-citationneurogenetics/SAHAdata: v0.99.1
Acri, D. J., Mustaklem, R., & Horan-Portelance, L. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20070341 · Cited by 1 · see citing works
Self-citationAcri, D. (2026) · Zenodo · doi:10.5281/zenodo.20691702 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- 10.1101/2024.10.31.24316513 · DOI · Paper page
- Authors
- Acri, D. J., Mustaklem, R., Horan-Portelance, L., Dabin, L. C., Park, J. H., Hartigan, K. A., Kersey, H. N., Mesecar, M. E., Gibbs, J. R., Cookson, M. R., & Kim, J.
- Affiliations
- National Institutes of Health (US)
- Indiana University School of Medicine
- Corresponding author
- Acri, Dominic J.
- ORCID
- Venue
- bioRxiv
- Publisher
- openRxiv
- Published
- 2026-08-04
- OpenAlex
- W7172539623
- Semantic Scholar
- 0904f69bbaf993c692b6bc3f2add2b985afba989
- Cited by
- 0
- References
- 65
- Type
- posted content
- Open access
- green
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- cell type annotation, label transfer, semi-automatic annotation, single-cell RNA sequencing, spatial transcriptomics, single-nucleus RNA sequencing, mouse brain, cerebellum, cerebral cortex, peripheral blood mononuclear cells, R package
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.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-17.
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
