1
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Howard D, Chameh HM, Guet-McCreight A, Hsiao HA, Vuong M, Seo YS, Shah P, Nigam A, Chen Y, Davie M, Hay E, Valiante TA, Tripathy SJ. An in vitro whole-cell electrophysiology dataset of human cortical neurons. Gigascience 2022; 11:giac108. [PMID: 36377463 PMCID: PMC9664072 DOI: 10.1093/gigascience/giac108] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/11/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Whole-cell patch-clamp electrophysiology is an essential technique for understanding how single neurons translate their diverse inputs into a functional output. The relative inaccessibility of live human cortical neurons for experimental manipulation has made it difficult to determine the unique features of how human cortical neurons differ from their counterparts in other species. FINDINGS We present a curated repository of whole-cell patch-clamp recordings from surgically resected human cortical tissue, encompassing 118 neurons from 35 individuals (age range, 21-59 years; 17 male, 18 female). Recorded human cortical neurons derive from layers 2 and 3 (L2&3), deep layer 3 (L3c), or layer 5 (L5) and are annotated with a rich set of subject and experimental metadata. For comparison, we also provide a limited set of comparable recordings from 21-day-old mice (11 cells from 5 mice). All electrophysiological recordings are provided in the Neurodata Without Borders (NWB) format and are available for further analysis via the Distributed Archives for Neurophysiology Data Integration online repository. The associated data conversion code is made publicly available and can help others in converting electrophysiology datasets to the open NWB standard for general reuse. CONCLUSION These data can be used for novel analyses of biophysical characteristics of human cortical neurons, including in cross-species or cross-lab comparisons or in building computational models of individual human neurons.
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Affiliation(s)
- Derek Howard
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | | | - Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Huan Allen Hsiao
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Maggie Vuong
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Young Seok Seo
- Krembil Brain Institute, University Health Network, Toronto, ON, M5T 1M8, Canada
| | - Prajay Shah
- Krembil Brain Institute, University Health Network, Toronto, ON, M5T 1M8, Canada
| | - Anukrati Nigam
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Institute of Medical Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Yuxiao Chen
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Melanie Davie
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Institute of Medical Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON, M5T 1M8, Canada
- Institute of Medical Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, M5S 1A4, Canada
- Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada
- Max Planck–University of Toronto Center for Neural Science and Technology, Toronto, ON, M5S 1A4, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON , M5S 1A4, Canada
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, M5T 1R8, Canada
- Institute of Medical Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
- Department of Psychiatry, University of Toront, Toronto, ON, M5T 1R8, Canada
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2
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Clarke RW. Theory of cell membrane interaction with glass. Phys Rev E 2021; 103:032401. [PMID: 33862714 DOI: 10.1103/physreve.103.032401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 01/19/2021] [Indexed: 11/07/2022]
Abstract
There are three regimes of cell membrane interaction with glass: Tight and loose adhesion, separated by repulsion. Explicitly including hydration, this paper evaluates the pressure between the surfaces as functions of distance for ion correlation and ion-screened electrostatics and electromagnetic fluctuations. The results agree with data for tight adhesion energy (0.5-3 vs 0.4-4 mJ/m^{2}), detachment pressure (7.9 vs. 9 MPa), and peak repulsion (3.4-7.5 vs. 5-10 kPa), also matching the repulsion's distance dependence on renormalization by steric pressure mainly from undulations.
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Affiliation(s)
- Richard W Clarke
- National Physical Laboratory, Teddington, TW11 0LW, United Kingdom
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3
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Gouwens NW, Sorensen SA, Baftizadeh F, Budzillo A, Lee BR, Jarsky T, Alfiler L, Baker K, Barkan E, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Crichton K, Daigle TL, Dalley R, de Frates RA, Dee N, Desta T, Lee SD, Dotson N, Egdorf T, Ellingwood L, Enstrom R, Esposito L, Farrell C, Feng D, Fong O, Gala R, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Graybuck L, Gu H, Hadley K, Hawrylycz MJ, Henry AM, Hill D, Hupp M, Kebede S, Kim TK, Kim L, Kroll M, Lee C, Link KE, Mallory M, Mann R, Maxwell M, McGraw M, McMillen D, Mukora A, Ng L, Ng L, Ngo K, Nicovich PR, Oldre A, Park D, Peng H, Penn O, Pham T, Pom A, Popović Z, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Robertson M, Ronellenfitch K, Ruiz A, Sandman D, Smith K, Sulc J, Sunkin SM, Szafer A, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Ward K, Williams G, Zhou Z, Ting JT, Arkhipov A, Sümbül U, Lein ES, Koch C, Yao Z, Tasic B, Berg J, Murphy GJ, Zeng H. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells. Cell 2020; 183:935-953.e19. [PMID: 33186530 PMCID: PMC7781065 DOI: 10.1016/j.cell.2020.09.057] [Citation(s) in RCA: 269] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 07/06/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022]
Abstract
Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.
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Affiliation(s)
| | | | | | - Agata Budzillo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian R Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lauren Alfiler
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kyla Berry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Kris Bickley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jasmine Bomben
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Braun
- Byte Physics, Schwarzastraße 9, Berlin 12055, Germany
| | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tsega Desta
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Rachel Enstrom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Colin Farrell
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Clare Gamlin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Melissa Gorham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lucas Graybuck
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Alex M Henry
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Madie Hupp
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tae Kyung Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Alice Mukora
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Osnat Penn
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Alice Pom
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zoran Popović
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | | | | | - Shea Ransford
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - David Sandman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Trinh
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Katelyn Ward
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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4
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Moradi K, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus 2020; 30:314-331. [PMID: 31472001 DOI: 10.1101/632760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/16/2019] [Accepted: 08/06/2019] [Indexed: 05/25/2023]
Abstract
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.
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Affiliation(s)
- Keivan Moradi
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - Giorgio A Ascoli
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
- Bioengineering Department, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
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5
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Moradi K, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus 2020; 30:314-331. [PMID: 31472001 PMCID: PMC7875289 DOI: 10.1002/hipo.23148] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/16/2019] [Accepted: 08/06/2019] [Indexed: 01/14/2023]
Abstract
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.
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Affiliation(s)
- Keivan Moradi
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
| | - Giorgio A. Ascoli
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
- Bioengineering Department, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
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6
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Erndt-Marino J, Yeisley DJ, Chen H, Levin M, Kaplan DL, Hahn MS. Interferon-Gamma Stimulated Murine Macrophages In Vitro: Impact of Ionic Composition and Osmolarity and Therapeutic Implications. Bioelectricity 2020; 2:48-58. [PMID: 32292895 PMCID: PMC7107958 DOI: 10.1089/bioe.2019.0032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background: Injections of osmolytes are promising immunomodulatory treatments for medical benefit, although the rationale and underlying mechanisms are often lacking. The goals of the present study were twofold: (1) to clarify the anti-inflammatory role of the potassium ion and (2) to begin to decouple the effects that ionic strength, ionic species, and osmolarity have on macrophage biology. Materials and Methods: RAW 264.7 murine macrophages were encapsulated in three-dimensional, poly(ethylene glycol) diacrylate hydrogels and activated with interferon-gamma to yield M(IFN). Gene and protein profiles were made of M(IFN) exposed to different hyperosmolar treatments (80 mM potassium gluconate, 80 mM sodium gluconate, and 160 mM sucrose). Results: Relative to M(IFN), all hyperosmolar treatments suppressed expression of pro-inflammatory markers (nitric oxide synthase-2 [NOS-2], tumor necrosis factor-alpha, monocyte chemoattractant protein-1 [MCP-1]) and increased messenger RNA (mRNA) expression of the pleiotropic and angiogenic markers interleukin-6 (IL-6) and vascular endothelial growth factor-A (VEGF), respectively. Ionic osmolytes also demonstrated a greater level of change compared to the nonionic treatments, with mRNA levels of IL-6 the most significantly affected. M(IFN) exposed to K+ exhibited the lowest levels of NOS-2 and MCP-1, and this ion limited IL-6 release induced by osmolarity. Conclusion: Cumulatively, these data suggest that osmolyte composition, ionic strength, and osmolarity are all parameters that can influence therapeutic outcomes. Future work is necessary to further decouple and mechanistically understand the influence that these biophysical parameters have on cell biology, including their impact on other macrophage functions, intracellular osmolyte composition, and cellular and organellular membrane potentials.
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Affiliation(s)
- Joshua Erndt-Marino
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts
- Department of Biology, Allen Discovery Center at Tufts University, Tufts University, Medford, Massachusetts
| | - Daniel J. Yeisley
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Hongyu Chen
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Michael Levin
- Department of Biology, Allen Discovery Center at Tufts University, Tufts University, Medford, Massachusetts
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts
- Department of Biology, Allen Discovery Center at Tufts University, Tufts University, Medford, Massachusetts
| | - Mariah S. Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
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7
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Bonzanni M, Payne SL, Adelfio M, Kaplan DL, Levin M, Oudin MJ. Defined extracellular ionic solutions to study and manipulate the cellular resting membrane potential. Biol Open 2020; 9:bio048553. [PMID: 31852666 PMCID: PMC6994931 DOI: 10.1242/bio.048553] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 12/04/2019] [Indexed: 12/19/2022] Open
Abstract
All cells possess an electric potential across their plasma membranes and can generate and receive bioelectric signals. The cellular resting membrane potential (RMP) can regulate cell proliferation, differentiation and apoptosis. Current approaches to measure the RMP rely on patch clamping, which is technically challenging, low-throughput and not widely available. It is therefore critical to develop simple strategies to measure, manipulate and characterize the RMP. Here, we present a simple methodology to study the RMP of non-excitable cells and characterize the contribution of individual ions to the RMP using a voltage-sensitive dye. We define protocols using extracellular solutions in which permeable ions (Na+, Cl- and K+) are substituted with non-permeable ions [N-Methyl-D-glucamine (NMDG), gluconate, choline, SO42-]. The resulting RMP modifications were assessed with both patch clamp and a voltage sensitive dye. Using an epithelial and cancer cell line, we demonstrate that the proposed ionic solutions can selectively modify the RMP and help determine the relative contribution of ionic species in setting the RMP. The proposed method is simple and reproducible and will make the study of bioelectricity more readily available to the cell biology community.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Mattia Bonzanni
- Department of Biomedical Engineering, Tufts University, Medford, 02155 MA, USA
- Allen Discovery Center, Tufts University, Medford, 02155 MA, USA
| | - Samantha L Payne
- Department of Biomedical Engineering, Tufts University, Medford, 02155 MA, USA
| | - Miryam Adelfio
- Department of Biomedical Engineering, Tufts University, Medford, 02155 MA, USA
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, 02155 MA, USA
- Allen Discovery Center, Tufts University, Medford, 02155 MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, 02155 MA, USA
| | - Madeleine J Oudin
- Department of Biomedical Engineering, Tufts University, Medford, 02155 MA, USA
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8
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Komendantov AO, Venkadesh S, Rees CL, Wheeler DW, Hamilton DJ, Ascoli GA. Quantitative firing pattern phenotyping of hippocampal neuron types. Sci Rep 2019; 9:17915. [PMID: 31784578 PMCID: PMC6884469 DOI: 10.1038/s41598-019-52611-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 09/20/2019] [Indexed: 01/19/2023] Open
Abstract
Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties. We augmented the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from published experiments. We designed and implemented objective protocols to classify firing patterns based on 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). This automated approach revealed 9 unique (plus one spurious) families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Novel statistical associations emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, experimental conditions, spike times, references to the original empirical evidences, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human- and machine-accessible form, will help design and interpret both experiments and model simulations.
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Affiliation(s)
- Alexander O Komendantov
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA.
| | - Siva Venkadesh
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Christopher L Rees
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Diek W Wheeler
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - David J Hamilton
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA.
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9
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Sprenger J, Zehl L, Pick J, Sonntag M, Grewe J, Wachtler T, Grün S, Denker M. odMLtables: A User-Friendly Approach for Managing Metadata of Neurophysiological Experiments. Front Neuroinform 2019; 13:62. [PMID: 31611781 PMCID: PMC6776611 DOI: 10.3389/fninf.2019.00062] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 09/02/2019] [Indexed: 11/29/2022] Open
Abstract
An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.
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Affiliation(s)
- Julia Sprenger
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | - Lyuba Zehl
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-1), Jülich Research Centre, Jülich, Germany
- Molecular and Systemic Neurophysiology, Department of Neurophysiology, Institute of Biology II, RWTH Aachen University, Aachen, Germany
| | - Jana Pick
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Michael Sonntag
- Department of Biology II, Ludwig-Maximilians-Universität München, Martinsried, Germany
| | - Jan Grewe
- Institut for Neurobiology, Abteilung Neuroethologie, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
| | - Thomas Wachtler
- Department of Biology II, Ludwig-Maximilians-Universität München, Martinsried, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | - Michael Denker
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
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10
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Gouwens NW, Sorensen SA, Berg J, Lee C, Jarsky T, Ting J, Sunkin SM, Feng D, Anastassiou CA, Barkan E, Bickley K, Blesie N, Braun T, Brouner K, Budzillo A, Caldejon S, Casper T, Castelli D, Chong P, Crichton K, Cuhaciyan C, Daigle TL, Dalley R, Dee N, Desta T, Ding SL, Dingman S, Doperalski A, Dotson N, Egdorf T, Fisher M, de Frates RA, Garren E, Garwood M, Gary A, Gaudreault N, Godfrey K, Gorham M, Gu H, Habel C, Hadley K, Harrington J, Harris JA, Henry A, Hill D, Josephsen S, Kebede S, Kim L, Kroll M, Lee B, Lemon T, Link KE, Liu X, Long B, Mann R, McGraw M, Mihalas S, Mukora A, Murphy GJ, Ng L, Ngo K, Nguyen TN, Nicovich PR, Oldre A, Park D, Parry S, Perkins J, Potekhina L, Reid D, Robertson M, Sandman D, Schroedter M, Slaughterbeck C, Soler-Llavina G, Sulc J, Szafer A, Tasic B, Taskin N, Teeter C, Thatra N, Tung H, Wakeman W, Williams G, Young R, Zhou Z, Farrell C, Peng H, Hawrylycz MJ, Lein E, Ng L, Arkhipov A, Bernard A, Phillips JW, Zeng H, Koch C. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 2019; 22:1182-1195. [PMID: 31209381 PMCID: PMC8078853 DOI: 10.1038/s41593-019-0417-0] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 04/25/2019] [Indexed: 12/21/2022]
Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.
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Affiliation(s)
| | | | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kris Bickley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nicole Blesie
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Thomas Braun
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Agata Budzillo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Dan Castelli
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Peter Chong
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tsega Desta
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Samuel Dingman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Michael Fisher
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Keith Godfrey
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Melissa Gorham
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Caroline Habel
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alex Henry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sam Josephsen
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tracy Lemon
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Xiaoxiao Liu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Medea McGraw
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sheana Parry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jed Perkins
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Sandman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Corinne Teeter
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rob Young
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Colin Farrell
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, USA.
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, USA
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11
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Farhoodi R, Lansdell BJ, Kording KP. Quantifying How Staining Methods Bias Measurements of Neuron Morphologies. Front Neuroinform 2019; 13:36. [PMID: 31191283 PMCID: PMC6541099 DOI: 10.3389/fninf.2019.00036] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
The process through which neurons are labeled is a key methodological choice in measuring neuron morphology. However, little is known about how this choice may bias measurements. To quantify this bias we compare the extracted morphology of neurons collected from the same rodent species, experimental condition, gender distribution, age distribution, brain region and putative cell type, but obtained with 19 distinct staining methods. We found strong biases on measured features of morphology. These were largest in features related to the coverage of the dendritic tree (e.g., the total dendritic tree length). Understanding measurement biases is crucial for interpreting morphological data.
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Affiliation(s)
- Roozbeh Farhoodi
- Department of Mathematics, Sharif University of Technology, Tehran, Iran
| | | | - Konrad Paul Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.,Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, United States
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12
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Tripathy SJ, Toker L, Bomkamp C, Mancarci BO, Belmadani M, Pavlidis P. Assessing Transcriptome Quality in Patch-Seq Datasets. Front Mol Neurosci 2018; 11:363. [PMID: 30349457 PMCID: PMC6187980 DOI: 10.3389/fnmol.2018.00363] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 09/13/2018] [Indexed: 12/21/2022] Open
Abstract
Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented 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 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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Affiliation(s)
- Shreejoy J. Tripathy
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Lilah Toker
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Claire Bomkamp
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - B. Ogan Mancarci
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Manuel Belmadani
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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