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Chadha R, Guerrero JA, Wei L, Sanchez LM. Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level. ACS Cent Sci 2024; 10:758-774. [PMID: 38680555 PMCID: PMC11046475 DOI: 10.1021/acscentsci.3c01438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 05/01/2024]
Abstract
This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.
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Affiliation(s)
- Rahuljeet
S Chadha
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125 United States
| | - Jason A. Guerrero
- Department
of Chemistry and Biochemistry, University
of California, Santa Cruz, Santa
Cruz, California 95064 United States
| | - Lu Wei
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125 United States
| | - Laura M. Sanchez
- Department
of Chemistry and Biochemistry, University
of California, Santa Cruz, Santa
Cruz, California 95064 United States
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2
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Xia J, Phan HV, Vistain L, Chen M, Khan AA, Tay S. Computational prediction of protein interactions in single cells by proximity sequencing. PLoS Comput Biol 2024; 20:e1011915. [PMID: 38483861 PMCID: PMC10939233 DOI: 10.1371/journal.pcbi.1011915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.
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Affiliation(s)
- Junjie Xia
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, United States of America
| | - Hoang Van Phan
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, United States of America
| | - Luke Vistain
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, United States of America
| | - Mengjie Chen
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America
- Department Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Aly A. Khan
- Department of Pathology, The University of Chicago, Chicago, Illinois, United States of America
| | - Savaş Tay
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, United States of America
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3
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Rimehaug AE, Stasik AJ, Hagen E, Billeh YN, Siegle JH, Dai K, Olsen SR, Koch C, Einevoll GT, Arkhipov A. Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. eLife 2023; 12:e87169. [PMID: 37486105 PMCID: PMC10393295 DOI: 10.7554/elife.87169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.
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Affiliation(s)
| | | | - Espen Hagen
- Department of Physics, University of OsloOsloNorway
- Department of Data Science, Norwegian University of Life SciencesÅsNorway
| | | | - Josh H Siegle
- MindScope Program, Allen InstituteSeattleUnited States
| | - Kael Dai
- MindScope Program, Allen InstituteSeattleUnited States
| | - Shawn R Olsen
- MindScope Program, Allen InstituteSeattleUnited States
| | - Christof Koch
- MindScope Program, Allen InstituteSeattleUnited States
| | - Gaute T Einevoll
- Department of Physics, University of OsloOsloNorway
- Department of Physics, Norwegian University of Life SciencesÅsNorway
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4
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Abstract
In addition to long-timescale rewiring, synapses in the brain are subject to significant modulation that occurs at faster timescales that endow the brain with additional means of processing information. Despite this, models of the brain like recurrent neural networks (RNNs) often have their weights frozen after training, relying on an internal state stored in neuronal activity to hold task-relevant information. In this work, we study the computational potential and resulting dynamics of a network that relies solely on synapse modulation during inference to process task-relevant information, the multi-plasticity network (MPN). Since the MPN has no recurrent connections, this allows us to study the computational capabilities and dynamical behavior contributed by synapses modulations alone. The generality of the MPN allows for our results to apply to synaptic modulation mechanisms ranging from short-term synaptic plasticity (STSP) to slower modulations such as spike-time dependent plasticity (STDP). We thoroughly examine the neural population dynamics of the MPN trained on integration-based tasks and compare it to known RNN dynamics, finding the two to have fundamentally different attractor structure. We find said differences in dynamics allow the MPN to outperform its RNN counterparts on several neuroscience-relevant tests. Training the MPN across a battery of neuroscience tasks, we find its computational capabilities in such settings is comparable to networks that compute with recurrent connections. Altogether, we believe this work demonstrates the computational possibilities of computing with synaptic modulations and highlights important motifs of these computations so that they can be identified in brain-like systems.
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Affiliation(s)
- Kyle Aitken
- Allen Institute, MindScope ProgramSeattleUnited States
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5
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Madi N, Chen D, Wolff R, Shapiro BJ, Garud NR. Community diversity is associated with intra-species genetic diversity and gene loss in the human gut microbiome. eLife 2023; 12:e78530. [PMID: 36757364 PMCID: PMC9977275 DOI: 10.7554/elife.78530] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023] Open
Abstract
How the ecological process of community assembly interacts with intra-species diversity and evolutionary change is a longstanding question. Two contrasting hypotheses have been proposed: Diversity Begets Diversity (DBD), in which taxa tend to become more diverse in already diverse communities, and Ecological Controls (EC), in which higher community diversity impedes diversification. Previously, using 16S rRNA gene amplicon data across a range of microbiomes, we showed a generally positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with the predictions of DBD (Madi et al., 2020). However, this positive 'diversity slope' plateaus at high levels of community diversity. Here we show that this general pattern holds at much finer genetic resolution, by analyzing intra-species strain and nucleotide variation in static and temporally sampled metagenomes from the human gut microbiome. Consistent with DBD, both intra-species polymorphism and strain number were positively correlated with community Shannon diversity. Shannon diversity is also predictive of increases in polymorphism over time scales up to ~4-6 months, after which the diversity slope flattens and becomes negative - consistent with DBD eventually giving way to EC. Finally, we show that higher community diversity predicts gene loss at a future time point. This observation is broadly consistent with the Black Queen Hypothesis, which posits that genes with functions provided by the community are less likely to be retained in a focal species' genome. Together, our results show that a mixture of DBD, EC, and Black Queen may operate simultaneously in the human gut microbiome, adding to a growing body of evidence that these eco-evolutionary processes are key drivers of biodiversity and ecosystem function.
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Affiliation(s)
- Naïma Madi
- Département de sciences biologiques, Université de MontréalMontréalCanada
| | - Daisy Chen
- Computational and Systems Biology, University of California, Los AngelesLos AngelesUnited States
- Bioinformatics and Systems Biology Program, University of California, San DiegoSan DiegoUnited States
| | - Richard Wolff
- Department of Ecology and Evolutionary Biology, University of California, Los AngelesLos AngelesUnited States
| | - B Jesse Shapiro
- Département de sciences biologiques, Université de MontréalMontréalCanada
- McGill Genome Centre, McGill UniversityMontrealCanada
- Quebec Centre for Biodiversity ScienceMontrealCanada
- McGill Centre for Microbiome ResearchMontrealCanada
- Department of Microbiology and Immunology, McGill UniversityMontrealCanada
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
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6
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Whye D, Wood D, Saber WA, Norabuena EM, Makhortova NR, Sahin M, Buttermore ED. A Robust Pipeline for the Multi-Stage Accelerated Differentiation of Functional 3D Cortical Organoids from Human Pluripotent Stem Cells. Curr Protoc 2023; 3:e641. [PMID: 36633423 PMCID: PMC9839317 DOI: 10.1002/cpz1.641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Disordered cellular development, abnormal neuroanatomical formations, and dysfunction of neuronal circuitry are among the pathological manifestations of cortical regions in the brain that are often implicated in complex neurodevelopmental disorders. With the advancement of stem cell methodologies such as cerebral organoid generation, it is possible to study these processes in vitro using 3D cellular platforms that mirror key developmental stages occurring throughout embryonic neurogenesis. Patterning-based stem cell models of directed neuronal development offer one approach to accomplish this, but these protocols often require protracted periods of cell culture to generate diverse cell types and current methods are plagued by a lack of specificity, reproducibility, and temporal control over cell derivation. Although ectopic expression of transcription factors offers another avenue to rapidly generate neurons, this process of direct lineage conversion bypasses critical junctures of neurodevelopment during which disease-relevant manifestations may occur. Here, we present a directed differentiation approach for generating human pluripotent stem cell (hPSC)-derived cortical organoids with accelerated lineage specification to generate functionally mature cortical neurons in a shorter timeline than previously established protocols. This novel protocol provides precise guidance for the specification of neuronal cell type identity as well as temporal control over the pace at which cortical lineage trajectories are established. Furthermore, we present assays that can be used as tools to interrogate stage-specific developmental signaling mechanisms. By recapitulating major components of embryonic neurogenesis, this protocol allows for improved in vitro modeling of cortical development while providing a platform that can be utilized to uncover disease-specific mechanisms of disordered development at various stages across the differentiation timeline. © 2023 Wiley Periodicals LLC. Basic Protocol 1: 3D hPSC neural induction Support Protocol 1: Neural rosette formation assay Support Protocol 2: Neurosphere generation Support Protocol 3: Enzymatic dissociation, NSC expansion, and cryopreservation Basic Protocol 2: 3D neural progenitor expansion Basic Protocol 3: 3D accelerated cortical lineage patterning and terminal differentiation.
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Affiliation(s)
- Dosh Whye
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
| | - Delaney Wood
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
| | - Wardiya Afshar Saber
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
- Department of Neurology, Harvard Medical School, Boston, MA
| | - Erika M. Norabuena
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
| | - Nina R. Makhortova
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
- Department of Neurology, Harvard Medical School, Boston, MA
| | - Mustafa Sahin
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
- Department of Neurology, Harvard Medical School, Boston, MA
| | - Elizabeth D. Buttermore
- Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Boston, MA
- F.M. Kirby Neurobiology Department, Boston Children’s Hospital, Boston, MA
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7
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Aitken K, Garrett M, Olsen S, Mihalas S. The geometry of representational drift in natural and artificial neural networks. PLoS Comput Biol 2022; 18:e1010716. [PMID: 36441762 PMCID: PMC9731438 DOI: 10.1371/journal.pcbi.1010716] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/08/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift differs from in-session variance and most often occurs along directions that have the most in-class variance, leading to a significant turnover in the neurons used for a given representation. Interestingly, despite this significant change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days. The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computational advantageous for the brain in the same way it is for artificial neural networks, e.g. preventing overfitting.
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Affiliation(s)
- Kyle Aitken
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
- * E-mail:
| | - Marina Garrett
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
| | - Shawn Olsen
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
| | - Stefan Mihalas
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
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8
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Donovan-Maiye RM, Brown JM, Chan CK, Ding L, Yan C, Gaudreault N, Theriot JA, Maleckar MM, Knijnenburg TA, Johnson GR. A deep generative model of 3D single-cell organization. PLoS Comput Biol 2022; 18:e1009155. [PMID: 35041651 PMCID: PMC8797242 DOI: 10.1371/journal.pcbi.1009155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 01/28/2022] [Accepted: 11/29/2021] [Indexed: 11/18/2022] Open
Abstract
We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.
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Affiliation(s)
| | - Jackson M. Brown
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Caleb K. Chan
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Liya Ding
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Calysta Yan
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Nathalie Gaudreault
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Julie A. Theriot
- Allen Institute for Cell Science, Seattle, Washington, United States of America
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America
| | - Mary M. Maleckar
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Theo A. Knijnenburg
- Allen Institute for Cell Science, Seattle, Washington, United States of America
- * E-mail:
| | - Gregory R. Johnson
- Allen Institute for Cell Science, Seattle, Washington, United States of America
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9
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Siegle JH, Ledochowitsch P, Jia X, Millman DJ, Ocker GK, Caldejon S, Casal L, Cho A, Denman DJ, Durand S, Groblewski PA, Heller G, Kato I, Kivikas S, Lecoq J, Nayan C, Ngo K, Nicovich PR, North K, Ramirez TK, Swapp J, Waughman X, Williford A, Olsen SR, Koch C, Buice MA, de Vries SEJ. Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology. eLife 2021; 10:e69068. [PMID: 34270411 PMCID: PMC8285106 DOI: 10.7554/elife.69068] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.
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Affiliation(s)
| | | | - Xiaoxuan Jia
- MindScope Program, Allen InstituteSeattleUnited States
| | | | | | | | - Linzy Casal
- MindScope Program, Allen InstituteSeattleUnited States
| | - Andy Cho
- MindScope Program, Allen InstituteSeattleUnited States
| | - Daniel J Denman
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | | | | | - Gregg Heller
- MindScope Program, Allen InstituteSeattleUnited States
| | - India Kato
- MindScope Program, Allen InstituteSeattleUnited States
| | - Sara Kivikas
- MindScope Program, Allen InstituteSeattleUnited States
| | - Jérôme Lecoq
- MindScope Program, Allen InstituteSeattleUnited States
| | - Chelsea Nayan
- MindScope Program, Allen InstituteSeattleUnited States
| | - Kiet Ngo
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Philip R Nicovich
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Kat North
- MindScope Program, Allen InstituteSeattleUnited States
| | | | - Jackie Swapp
- MindScope Program, Allen InstituteSeattleUnited States
| | - Xana Waughman
- MindScope Program, Allen InstituteSeattleUnited States
| | - Ali Williford
- MindScope Program, Allen InstituteSeattleUnited States
| | - Shawn R Olsen
- MindScope Program, Allen InstituteSeattleUnited States
| | - Christof Koch
- MindScope Program, Allen InstituteSeattleUnited States
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10
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Miller JA, Gouwens NW, Tasic B, Collman F, van Velthoven CTJ, Bakken TE, Hawrylycz MJ, Zeng H, Lein ES, Bernard A. Common cell type nomenclature for the mammalian brain. eLife 2020; 9:e59928. [PMID: 33372656 PMCID: PMC7790494 DOI: 10.7554/elife.59928] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/28/2020] [Indexed: 12/22/2022] Open
Abstract
The advancement of single-cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the common cell type nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step toward community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.
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