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Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Open accessAbstract
Summary Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-Seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell’s RNAs, and sequencing them all together. Drop-Seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts’ cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-Seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.
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“Here, high-throughput perturbation screens—based on RNA-seq,28,83,90 optical pooled screens (OPS),29,39,91 or other technologies—offer a rich resource through which the AIVC can be trained to predict these effects.”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [83]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our most important modification to the Drop-seq protocol was an alternative way to break droplets, which recovered beads much more efficiently than in the original publication of Drop-seq 7 .”
From Pooled CRISPR screening with single-cell transcriptome readout. (2017) · cites this paper as [7]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Because the top 50 PCs explained more than 90% of single-cell variance (Supplementary Figure S4), we chose them to cluster 175 MEFs by Seurat package (Macosko et al., 2015).”
From Single-Cell Transcriptome Analysis Reveals Six Subpopulations Reflecting Distinct Cellular Fates in Senescent Mouse Embryonic Fibroblasts (2020) · cites this paper as Macosko et al., 2015
The cited source says what the claim attributes to it.
In the citing paper
“While strategies to demultiplex cells from different species1, 10, 17 or host and graft samples17 have been reported, simultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [1]
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In the citing paper
“Droplet single cell RNA-sequencing (dscRNA-seq) has increased substantially the throughput of single cell capture and library preparation1, 10, enabling the simultaneous profiling of thousands of cells.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [1]
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In the citing paper
“single-cell RNA sequencing (scRNA-seq) has emerged as a powerful method for unbiased discovery of cell types and states based on gene activity (Islam et al., 2011, Jaitin et al., 2014, Macosko et al., 2015, Ramsköld et al., 2012, Shekhar et al., 2016, Tang et al., 2009, Tasic et al., 2016, Usoskin et al., 2015, Zeisel et al., 2015)”
From Molecular Architecture of the Mouse Nervous System (2018) · cites this paper as Macosko et al., 2015
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In the citing paper
“Implementing this concept, our CROP-seq method combines four key components: a gRNA vector that makes individual gRNAs detectable in single-cell RNA-seq experiments, a high-throughput assay for single-cell RNA-seq 7 , a computational pipeline for assigning single-cell transcriptomes to gRNAs, and a bioinformatic method for analyzing and interpreting gRNA-induced transcriptional profiles.”
From Pooled CRISPR screening with single-cell transcriptome readout. (2017) · cites this paper as [7]
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In the citing paper
“When we applied Dropseq 7 on a 1:1:1 mixture of the three cell lines, the resulting single-cell transcriptomes provided high sequencing coverage for the gRNAs, and we were able to assign single cells to gRNAs at the expected ratios (Fig. 1h).”
From Pooled CRISPR screening with single-cell transcriptome readout. (2017) · cites this paper as [7]
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In the citing 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.”
From Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans (2020) · cites this paper as Macosko et al., 2015
The cited source says what the claim attributes to it.
In the citing paper
“Although limited to two samples, as shown in our analysis, sex chromosomes are also an effective strategy to demultiplex 2 samples [43], as can the mixing of species—as in a barnyard experiment [11].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“We first investigate 2 “species mixing” datasets run on the Chromium 10X [2] and DropSeq [14] platforms, which allow us to directly identify contaminating mRNAs and test our method’s accuracy.”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [14]
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In the citing paper
“A similar effect is seen in the Drop-Seq–based species-mixing data (Supplementary Fig. S3).”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [14]
In the citing paper
“is implemented here for 10X Genomics15 and Drop-seq16.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [16]
In the citing paper
“Notably, in the context of this study, the C1 system also allows for direct confirmation of single cell capture events, in contrast to most other microfluidic-based approaches322.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [3]
In the citing paper
“The automated C1 microfluidic platform (Fluidigm), while more expensive per sample, has been shown to confer several advantages over platforms that make use of droplets to capture single cells321. In particular, smaller samples can be processed using the C1 (when cell numbers are limiting), and the C1 capture efficiency of genes (and RNA molecules) is markedly higher.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [3]
In the citing paper
“Other methods, such as DROP-seq [175], can profile more than 10,000 cells at a time.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [175]
In the citing paper
“The scale and precision of such studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq30,31 and millions of cells for mass cytometry signature protein measurements32–34.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“These can manifest as high-quality cells by many measures (more transcripts, more complex libraries) and lead to erroneous biological conclusions, such as false detection of ‘hybrids’—cells that seem to be an intermediate type between two distinct cell populations but are in fact a mixture of RNA from two or more cells (A.R. and colleagues30).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“However, although spike-in controls provide important information59, they do not control for any technical variation preceding their introduction, especially tissue acquisition, dissociation, sorting and lysis30.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“Through deconvolution of complex samples30, such as tumor biopsies35, one may infer their cellular milieu, and characterize the rare5, functionally important1, and unknown36 cell types they contain.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“single-cell profiles provide a near-continuous sampling and show high conservation across cell types and species30,97,99”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“Using the latest protocols for massively parallel scRNA-seq30,31 and mass cytometry32–34, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [30]
In the citing paper
“Together, these results suggest the dependence of fibroblast proliferation, which is a key step in fibrogenesis38, on trophism exerted by PDGF-AA+ macrophages recruited to sites of injury.”
From Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage (2019) · cites this paper as [38]
In the citing paper
“Single cell transcriptomics enables unbiased, high-throughput quantitative surveys of molecularly defined cell types4–6 that can be applied to any species including human.”
From Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type (2018) · cites this paper as [6]
In the citing paper
“massively parallel scRNA-seq methods, such as Drop-seq7 and related methods8–10 can be readily applied at scale11 in a cost-effective manner12, but require intact single cell suspension as input.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [7]
In the citing paper
“Transcriptomically derived cell type taxonomies in the adult mammalian brain, with majority of the studies conducted in mouse, have consistently revealed a hierarchical organization of cell types (Fig. 2) (Brain Initiative Cell Census Network, 2021; Macosko et al., 2015; Romanov et al., 2017; Russ et al., 2021; Saunders et al., 2018; Shekhar et al., 2016; Tasic et al., 2016; Tasic et al., 2018;”
From What is a cell type and how to define it? (2022) · cites this paper as Macosko et al., 2015
In the citing paper
“However, it is only in the past 4 years that large-scale scRNAseq technology has been able to provide a comprehensive molecular analysis of neuronal diversity20,21.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [20]
In the citing paper
“In 220 neurons, we recaptured subsets of GABA, glutamate and dopamine cells that contained genes for alternative neurotransmission”
From Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes (2017) · cites this paper as [22]
In the citing paper
“Drop-seq data processing of 220 neurons as output was performed using the Drop-seq Tools v1.12 software22.”
From Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes (2017) · cites this paper as [22]
In the citing paper
“Droplet microfluidic (Klein et al., 2015; Macosko et al., 2015; Zheng et al., 2017) and well-based (Cao et al., 2017; Gierahn et al., 2017; Han et al., 2018; Rosenberg et al., 2018) technologies now enable the relatively inexpensive, high-throughput isolation and barcoding of cell transcriptomes.”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Macosko et al., 2015
In the citing paper
“This dataset, though not representative of most single-cell experiments, provides a useful test case because the differences between human and mouse genomic sequence provide an independent way to detect doublets (Klein et al., 2015; Macosko et al., 2015).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Macosko et al., 2015
In the citing paper
“In a typical scRNA-seq experiment, at least several percent of all capture events are multiplets (Cao et al., 2017; Klein et al., 2015; Macosko et al., 2015; Zheng et al., 2017).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Macosko et al., 2015
In the citing paper
“The field has rapidly developed ever since and is divided into two major categories: microfluidics droplet-based transcriptomics7–10 and microwell plate-based11–13 transcriptomics (Box 1).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [9]
In the citing paper
“We used lists of conserved genes from a well-established dataset for HeLa cell lines as the “seeds” to reconstruct 5 cell cycle phases along microglial division pseudotime (Macosko et al., 2015; Whitfield et al., 2002).”
From Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing (2019) · cites this paper as Macosko et al., 2015
In the citing paper
“Typically, PCA summarizes a dataset via its top N principal components, where N can be determined by “elbow” heuristics (see Fig 4F) or the permutation‐test‐based jackstraw method (Chung & Storey, 2015; Macosko et al, 2015).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Macosko et al, 2015
In the citing paper
“Notably, the down-regulation of ribosomal genes and bulk protein synthesis has been long considered as a hallmark of aging 54,55 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [55]
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Citations
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
(1996) · Cited by 20,179
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationmRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Self-citationHigh-throughput droplet digital PCR system for absolute quantitation of DNA copy number
Hindson, B. J., Ness, K. D., Masquelier, D. A., Belgrader, P., Heredia, N. J., Makarewicz, A. J., Bright, I. J., Lucero, M. Y., Hiddessen, A. L., Legler, T. C., et al. (2011) · Analytical Chemistry · doi:10.1021/ac202028g · Cited by 2,792
Britten, R. J., & Kohne, D. E. (1968) · Science · doi:10.1126/science.161.3841.529 · Cited by 2,789
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Dynamic Pattern Formation in a Vesicle-Generating Microfluidic Device
Thorsen, T., Roberts, R. W., Arnold, F. H., & Quake, S. R. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.4163 · Cited by 2,052
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
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Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27
Chen, W., Wang, X., Wei, G., Huang, Y., Shi, Y., Li, D., Qiu, S., Zhou, B., Cao, J., Chen, M., et al. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00867 · Cited by 26
Fundamental limits on dynamic inference from single cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2017) · doi:10.1101/170118 · Cited by 27
Spatial transcriptome sequencing of FFPE tissues at cellular level
Liu, Y., Enninful, A., Deng, Y., & Fan, R. (2020) · doi:10.1101/2020.10.13.338475 · Cited by 26
IDOL regulates systemic energy balance through control of neuronal VLDLR expression
Lee, S. D., Priest, C., Bjursell, M., Gao, J., Arneson, D. V., Ahn, I. S., Diamante, G., van Veen, J. E., Massa, M. G., Calkin, A. C., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0127-7 · Cited by 26
Venugopal, G., Bird, J. T., Washam, C. L., Roys, H., Bowlin, A., Byrum, S. D., & Weinkopff, T. (2022) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0010518 · Cited by 26
Balakumar, A., Das, D., Datta, A., Mishra, A., Bryak, G., Ganesh, S. M., Netea, M. G., Kumar, V., Lionakis, M. S., Arora, D., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012699 · Cited by 25
Dumeaux, V., Massahi, S., Bettauer, V., Mottola, A., Dukovny, A., Khurdia, S. S., Costa, A. C. B. P., Omran, R. P., Simpson, S., Xie, J. L., et al. (2023) · eLife · doi:10.7554/elife.81406 · Cited by 25
Kaulich, E., Waselenchuk, Q., Fürst, N., Desch, K., Mosbacher, J., Ciirdaeva, E., Juengling, M., Tushev, G., Langer, J., & Schuman, E. M. (2024) · doi:10.1101/2024.08.05.606570 · Cited by 22
Stern, E., Ruf-Zamojski, F., Zalepa-King, L., Pincas, H., Choi, S. G., Peskin, C. S., Hayot, F., Turgeon, J. L., & Sealfon, S. C. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.783886 · Cited by 21
Evaluation of genetic demultiplexing of single-cell sequencing data from model species
Cardiello, J. F., Joven Araus, A., Giatrellis, S., Helsens, C., Simon, A., & Leigh, N. D. (2023) · Life Science Alliance · doi:10.26508/lsa.202301979 · Cited by 21
“Over the last decade, single-cell RNA sequencing (scRNA-seq) has exploded in popularity as a species agnostic tool for studying gene expression at the level of individual cells (Klein et al, 2015; Macosko et al, 2015; Villani et al, 2017; Han et al, 2018).”
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Landa, B., Zhang, T. T. C. K., & Kluger, Y. (2022) · SIAM Journal on Mathematics of Data Science · doi:10.1137/21m1456807 · Cited by 20
Wang, W., Cen, Y., Lu, Z., Xu, Y., Sun, T., Xiao, Y., Liu, W., Li, J. J., & Wang, C. (2024) · Genome Biology · doi:10.1186/s13059-024-03284-w · Cited by 19
Demuxalot: scaled up genetic demultiplexing for single-cell sequencing
Rogozhnikov, A., Ramkumar, P., Shah, K., Bedi, R., Kato, S., & Escola, G. S. (2021) · doi:10.1101/2021.05.22.443646 · Cited by 19
Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering
Vallejo, A. F., Davies, J., Grover, A., Tsai, C.-H., Jepras, R., Polak, M. E., & West, J. (2019) · doi:10.1101/800631 · Cited by 17
Therapeutic Idol reduction ameliorates amyloidosis and improves cognitive function in APP/PS1 mice
Gao, J., Littman, R., Diamante, G., Xiao, X., Ahn, I. S., Yang, X., Cole, T. A., & Tontonoz, P. (2020) · Molecular and Cellular Biology · doi:10.1128/mcb.00518-19 · Cited by 15
The biology of mental disorders: progress at last
Hyman, S. E. (2023) · Daedalus · doi:10.1162/daed_a_02038 · Cited by 15
Targeting individual cells by barcode in pooled sequence libraries
Ranu, N., Villani, A.-C., Hacohen, N., & Blainey, P. C. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky856 · Cited by 13
Direct capture of CRISPR guides enables scalable, multiplexed, and multi-omic Perturb-seq
Replogle, J. M., Xu, A., Norman, T. M., Meer, E. J., Terry, J. M., Riordan, D., Srinivas, N., Mikkelsen, T. S., Weissman, J. S., & Adamson, B. (2018) · doi:10.1101/503367 · Cited by 465
Expression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11
Multi-omic analyses in immune cell development with lessons learned from T cell development
Cordes, M., Pike-Overzet, K., Van Den Akker, E. B., Staal, F. J. T., & Canté-Barrett, K. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1163529 · Cited by 9
Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease
Kitani, A., Matsui, Y., & Initiative, F. T. A. D. N. (2025) · Alzheimer's Research & Therapy · doi:10.1186/s13195-025-01705-x · Cited by 6
Yuan, Z., Liu, Z., Zhou, M., Wen, H., & Li, B. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105111 · Cited by 6
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4
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Feng, Y., Wu, Y., Zhu, Y., He, Y., & Weng, W. (2025) · Experimental Eye Research · doi:10.1016/j.exer.2025.110436 · Cited by 3
Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data
Fu, Y., Youness, M., Virzì, A., Song, X., Tubeeckx, M. R. L., De Keulenaer, G. W., Heidbuchel, H., Segers, V. F. M., Sipido, K. R., Thienpont, B., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf371 · Cited by 2
“Although limited to two samples, as shown in our analysis, sex chromosomes are also an effective strategy to demultiplex 2 samples [43], as can the mixing of species—as in a barnyard experiment [11].”
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“Droplet-based scRNA-seq protocols have become particularly popular due to their high-throughput and low cost per cell [1,2,6].”
Metadata
- Authors
- Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., Trombetta, J. J., Weitz, D. A., Sanes, J. R., Shalek, A. K., Regev, A., & McCarroll, S. A.
- Affiliations
- Broad Institute (US)
- Boston Children's Hospital (US)
- Harvard University (US)
- Corresponding author
- Macosko, Evan Z.
- McCarroll, Steven A.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 161
- Issue
- 5
- Pages
- 1202-1214
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4481139
- OpenAlex
- W2102212449
- Semantic Scholar
- 9227690267ab90da9a0af109e170537ce843cd5d
- Cited by
- 7,110
- References
- 48
- Citations by year
- 2026: 445 · 2025: 567 · 2024: 698 · 2023: 761 · 2022: 789 · 2021: 902 · 2020: 854 · 2019: 710 · 2018: 642 · 2017: 413 · 2016: 245 · 2015: 67
- FWCI
- 198.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (ECS- 03-35765, 1310266, 0335765, 1420570)
- Howard Hughes Medical Institute
- Alexander and Margaret Stewart Trust
- National Institutes of Health (ECS-0335765, DMR-1420570)
- Materials Research Science and Engineering Center, Harvard University (DMR- 1420570, 1420570, DMR-1310266)
- Stanley Center for Psychiatric Research, Broad Institute
- Klarman Cell Observatory, Broad Institute
- Simons Center for the Social Brain, Massachusetts Institute of Technology
- National Institute of Mental Health (R25MH094612, U01MH105960)
- National Human Genome Research Institute (P50HG006193, HG006193)
- National Institute of Allergy and Infectious Diseases (T32AI074549)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- Division of Materials Research (DMR 14–20570, DMR-1310266, 1420570, 1310266)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Innovative Microfluidic and Catalytic Techniques Innovation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Drop-seq, single-cell transcriptomics, nanoliter droplets, cell barcoding, mRNA sequencing, retinal cell types, retinal transcriptome, cell subtypes, molecular atlas, mouse retina
- MeSH terms
- Animals, Retina, Sequence Analysis, RNA, Gene Expression Profiling, Microfluidic Analytical Techniques, Mice, Genome-Wide Association Study, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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 ›
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