Assessing Transcriptome Quality in Patch-Seq Datasets
Open accessAbstract
Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented single-cell access to a neuron’s transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq single-cell transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples.
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mRNA collected from one neuron via Patch-seq often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.
“we (Figure S2B) and others (Tripathy et al., 2018) found that the mRNA collected from one neuron via a Patch-seq recording often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Tripathy et al., 2018
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179 · see citing works
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Zhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Pfeffer, C. K., Xue, M., He, M., Huang, Z. J., & Scanziani, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3446 · Cited by 1,353 · see citing works
Analysis of gene expression in single live neurons.
Eberwine, J. H., Yeh, H., Miyashiro, K., Cao, Y., Nair, S., Finnell, R. H., Zettel, M. F., & Coleman, P. D. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.7.3010 · Cited by 953 · see citing works
Molecular and Physiological Diversity of Cortical Nonpyramidal Cells
Cauli, B., Audinat, E., Lambolez, B., Angulo, M. C., Ropert, N., Tsuzuki, K., Hestrin, S., & Rossier, J. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-10-03894.1997 · Cited by 738 · see citing works
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3445 · Cited by 581 · see citing works
Madry, C., Kyrargyri, V., Arancibia-Cárcamo, I. L., Jolivet, R., Kohsaka, S., Bryan, R. M., & Attwell, D. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.002 · Cited by 464 · see citing works
Classification of electrophysiological and morphological neuron types in the mouse visual cortex
Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0417-0 · Cited by 456 · see citing works
Detecting Activated Cell Populations Using Single-Cell RNA-Seq
Wu, Y. E., Pan, L., Zuo, Y., Li, X., & Hong, W. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.026 · Cited by 438 · see citing works
Normalizing single-cell RNA sequencing data: challenges and opportunities
Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4292 · Cited by 436 · see citing works
Schulz, D. J., Goaillard, J.-M., & Marder, E. (2006) · Nature Neuroscience · doi:10.1038/nn1639 · Cited by 426 · see citing works
Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3443 · Cited by 386 · see citing works
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365 · see citing works
Classification of fusiform neocortical interneurons based on unsupervised clustering
Cauli, B., Porter, J. T., Tsuzuki, K., Lambolez, B., Rossier, J., Quenet, B., & Audinat, E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.11.6144 · Cited by 316 · see citing works
Disentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293 · see citing works
Toledo-Rodriguez, M. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh092 · Cited by 241 · see citing works
Diversity of Interneurons in the Dorsal Striatum Revealed by Single-Cell RNA Sequencing and PatchSeq
Muñoz-Manchado, A. B., Bengtsson Gonzales, C., Zeisel, A., Munguba, H., Bekkouche, B., Skene, N. G., Lönnerberg, P., Ryge, J., Harris, K. D., Linnarsson, S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.053 · Cited by 229 · see citing works
mRNA transport & local translation in neurons
Glock, C., Heumüller, M., & Schuman, E. M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.05.005 · Cited by 199 · see citing works
Swensen, A. M., & Bean, B. P. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.3929-04.2005 · Cited by 197 · see citing works
Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons
Földy, C., Darmanis, S., Aoto, J., Malenka, R. C., Quake, S. R., & Südhof, T. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610155113 · Cited by 179 · see citing works
Bardy, C., van den Hurk, M., Kakaradov, B., Erwin, J. A., Jaeger, B. N., Hernandez, R. V., Eames, T., Paucar, A. A., Gorris, M. A. J., Marchand, C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.158 · Cited by 160 · see citing works
Cadwell, C. R., Scala, F., Li, S., Livrizzi, G., Shen, S., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · Nature Protocols · doi:10.1038/nprot.2017.120 · Cited by 147 · see citing works
Mancarci, B. O., Toker, L., Tripathy, S. J., Li, B., Rocco, B. R., Sibille, E. L., & Pavlidis, P. (2017) · eNeuro · doi:10.1523/eneuro.0212-17.2017 · Cited by 138 · see citing works
Self-citationOkaty, B. W., Sugino, K., & Nelson, S. B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016493 · Cited by 104 · see citing works
Rossier, J., Bernard, A., Cabungcal, J.-H., Perrenoud, Q., Savoye, A., Gallopin, T., Hawrylycz, M., Cuénod, M., Do, K., Urban, A., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.162 · Cited by 101 · see citing works
Kodama, T., Guerrero, S., Shin, M., Moghadam, S. D., Faulstich, M., & du Lac, S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0543-12.2012 · Cited by 84 · see citing works
PCR and patch-clamp analysis of single neurons
Sucher, N. J., & Deitcher, D. L. (1995) · Neuron · doi:10.1016/0896-6273(95)90257-0 · Cited by 78 · see citing works
Transcriptomic correlates of neuron electrophysiological diversity
Tripathy, S. J., Toker, L., Li, B., Crichlow, C.-L., Tebaykin, D., Mancarci, B. O., & Pavlidis, P. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005814 · Cited by 69 · see citing works
Self-citationAssessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing dataPreprint
Lun, A. T. L., Calero‐Nieto, F. J., Haim-Vilmovsky, L., Göttgens, B., & Marioni, J. C. (2017) · Genome Research · doi:10.1101/gr.222877.117 · Cited by 61 · see citing works
van den Hurk, M., Erwin, J. A., Yeo, G. W.-M., Gage, F. H., & Bardy, C. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00261 · Cited by 42 · see citing works
Chen, X., Zhang, K., Zhou, L., Gao, X., Wang, J., Yao, Y., He, F., Luo, Y., Yu, Y., Li, S., et al. (2016) · Protein & Cell · doi:10.1007/s13238-016-0247-8 · Cited by 39 · see citing works
Toledo‐Rodriguez, M., & Markram, H. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1096-0_8 · Cited by 35 · see citing works
Subkhankulova (2010) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2010.00010 · Cited by 35 · see citing works
The Microtubule Regulator NEK7 Coordinates the Wiring of Cortical Parvalbumin Interneurons
Hinojosa, A. J., Deogracias, R., & Rico, B. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.115 · Cited by 20 · see citing works
Pfeffer, C. K., & Beltramo, R. (2017) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2017.00376 · Cited by 16 · see citing works
Tebaykin, D., Tripathy, S. J., Binnion, N., Li, B., Gerkin, R. C., & Pavlidis, P. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00604.2017 · Cited by 15 · see citing works
Self-citationQ&A: using Patch-seq to profile single cells
Cadwell, C. R., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · BMC Biology · doi:10.1186/s12915-017-0396-0 · Cited by 13 · see citing works
Tasic, B., Levi, B. P., & Menon, V. (2017) · Springer eBooks · doi:10.1007/978-3-319-57363-2_18 · Cited by 10 · see citing works
A critical assessment of single-cell transcriptomes sampled following patch-clamp electrophysiologyPreprint
Tripathy, S. J., Toker, L., Bomkamp, C., Mancarci, B. O., Belmadani, M., & Pavlidis, P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/298133 · Cited by 1 · see citing works
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Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538 · see citing works
“However, we (Figure S2B) and others (Tripathy et al., 2018) found that the mRNA collected from one neuron via a Patch-seq recording often includes contaminating mRNA from adjacent neurons and/or non-neuronal cells.”
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
“2e): cells with the highest correlation below 0.4 (78 cells); cells that would be assigned to non-neural t-types, presumably owing to RNA contamination 51 (14 cells; see also Extended Data Fig.”
InfluentialHuman neocortical expansion involves glutamatergic neuron diversification
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285 · see citing works
Lee, J., Wang, Z., Messi, M. L., Milligan, C., Furdui, C. M., & Delbono, O. (2024) · Acta Physiologica · doi:10.1111/apha.14123 · Cited by 15 · see citing works
- Authors
- Tripathy, S. J., Toker, L., Bomkamp, C., Mancarci, B. O., Belmadani, M., & Pavlidis, P.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Tripathy, Shreejoy J.
- Pavlidis, Paul
- ORCID
- Venue
- Frontiers in Molecular Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2018-10-08
- Volume
- 11
- Pages
- 363-363
- ISSN
- 1662-5099 (ISSN-L)
- PMC
- PMC6187980
- OpenAlex
- W2891983486
- Cited by
- 39
- References
- 46
- Citations by year
- 2026: 4 · 2024: 6 · 2023: 7 · 2022: 4 · 2021: 7 · 2020: 8 · 2019: 3
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Molecular Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Kids Brain Health Network
- National Institutes of Health
- Canadian Institutes of Health Research (FDN143206)
- National Institute of Mental Health (R01MH111099)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Patch-seq, stem cell-derived neurons
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