Integrative analysis of in vivo recording with single-cell RNA-seq data reveals molecular properties of light-sensitive neurons in mouse V1

Integrating physiological and transcriptomic analyses at the single-neuron level

Neurons generate various spike patterns to execute different functions. Understanding how these physiological neuronal spike patterns are related to their molecular characteristics is a long-standing issue in neuroscience. Herein, we review the results of recent studies that have addressed this issue by integrating physiological and transcriptomic techniques. A sequence of experiments, including in vivo recording and/or labeling, brain tissue slicing, cell collection, and transcriptomic analysis, have identified the gene expression profiles of brain neurons at the single-cell level, with activity patterns recorded in living animals. Although these techniques are still in the early stages, this methodological idea is principally applicable to various brain regions and neuronal activity patterns. Accumulating evidence will contribute to a deeper understanding of neuronal characteristics by integrating insights from molecules to cells, circuits, and behaviors.

A deep learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

A method to analyze gene expression profiles from hippocampal neurons electrophysiologically recorded in vivo

Hippocampal pyramidal neurons exhibit diverse spike patterns and gene expression profiles. However, their relationships with single neurons are not fully understood. In this study, we designed an electrophysiology-based experimental procedure to identify gene expression profiles using RNA sequencing of single hippocampal pyramidal neurons whose spike patterns were recorded in living mice. This technique involves a sequence of experiments consisting of in vivo juxtacellular recording and labeling, brain slicing, cell collection, and transcriptome analysis. We demonstrated that the expression levels of a subset of genes in individual hippocampal pyramidal neurons were significantly correlated with their spike burstiness, submillisecond-level spike rise times or spike rates, directly measured by in vivo electrophysiological recordings. Because this methodological approach can be applied across a wide range of brain regions, it is expected to contribute to studies on various neuronal heterogeneities to understand how physiological spike patterns are associated with gene expression profiles.

Most discriminative stimuli for functional cell type clustering

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Patch-seq: Advances and Biological Applications

Multimodal analysis of gene-expression patterns, electrophysiological properties, and morphological phenotypes at the single-cell/single-nucleus level has been arduous because of the diversity and complexity of neurons. The emergence of Patch-sequencing (Patch-seq) directly links transcriptomics, morphology, and electrophysiology, taking neuroscience research to a multimodal era. In this review, we summarized the development of Patch-seq and recent applications in the cortex, hippocampus, and other nervous systems. Through generating multimodal cell type atlases, targeting specific cell populations, and correlating transcriptomic data with phenotypic information, Patch-seq has provided new insight into outstanding questions in neuroscience. We highlight the challenges and opportunities of Patch-seq in neuroscience and hope to shed new light on future neuroscience research.

Combinatorial genetic strategies for dissecting cell lineages, cell types, and gene function in the mouse brain

Research in neuroscience has greatly benefited from the development of genetic approaches that enable lineage tracing, cell type targeting, and conditional gene regulation. Recent advances in combinatorial strategies, which integrate multiple cellular features, have significantly enhanced the spatiotemporal precision and flexibility of these manipulations. In this minireview, we introduce the concept and design of these strategies and provide a few examples of their application in genetic fate mapping, cell type targeting, and reversible conditional gene regulation. These advancements have facilitated in-depth investigation into the developmental principles underlying the assembly of brain circuits, granting experimental access to highly specific cell lineages and subtypes, as well as offering valuable new tools for modeling and studying neurological diseases. Additionally, we discuss future directions aimed at expanding and improving the existing genetic toolkit for a better understanding of the development, structure, and function of healthy and diseased brains.

Linking transcriptomes with morphological and functional phenotypes in mammalian retinal ganglion cells

Retinal ganglion cells (RGCs) are the brain's gateway to the visual world. They can be classified into different types on the basis of their electrophysiological, transcriptomic, or morphological characteristics. Here, we characterize the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch sequencing (Patch-seq), providing functional and morphological annotation of many transcriptomic-defined cell types of a previously established RGC atlas. We show a convergence of different modalities in defining the RGC identity and reveal the degree of correspondence for well-characterized cell types across multimodal data. Moreover, we complement some RGC types with detailed morphological and functional properties. We also identify differentially expressed genes among ON, OFF, and ON-OFF RGCs such as Vat1l, Slitrk6, and Lmo7, providing candidate marker genes for functional studies. Our research suggests that the molecularly distinct clusters may also differ in their roles of encoding visual information.

Re-defining and tackling the emerging challenges in stem cell research and translation: A report of the 10th CSSCR annual meeting

The 10th congress of the Chinese Society for Stem Cell Research was held from 11 to 13 October 2020 at Guiyang International Eco‐Conference Center in Guizhou (Southwest China) with 700 offline participants (and 500 online participants). With a theme adopted from a poem by Mao Zedong during the long march, ‘However difficult it might appear; the challenges will be overcome’, the congress provided opportunities for the fast‐growing field to exchange ideas among people from academia, industry and regulatory authorities, to help accelerate translation. Eight plenary lectures and six concurrent sessions on cell fate decision, stem cell metabolism, neural stem cells and neural regeneration, organoids and disease models, tissue and organ regeneration and clinical translation of stem cell research were covered, including 77 oral presentations and 75 poster presentations. The congress also included special programmes of a youth forum, a lecture award programme, a flagship journal forum and a dedicated networking session. This 3‐day event will significantly boost the stem cell research in an era closing to application.

Live Cell Genomics: Cell-Specific Transcriptome Capture in Live Single Cells from Complex Tissues

Analysis of single-cell transcriptomes shows the single-cell heterogeneity between cells within a population which is vital to our understanding of normal function and disease development. To obtain single-cell transcriptome profiling, however, the poly-A RNA must be accurately isolated from the target cell. We developed a single-cell analysis procedure called transcriptome in vivo analysis (TIVA), which will allow accurate characterization of targeted cell-specific transcriptomes from live tissue. This is accomplished using a RNA capture molecule called TIVA tag that captures the transcriptome of selected cells in their natural microenvironment. An important aspect of the TIVA approach is that the tag is delivered into the cytoplasm of live cells using cell-penetrating peptides (CPPs). Once the TIVA tag is in the cellular cytoplasm, it binds to mRNA after photoactivation of the compound. Using CPPs in combination with photoactivation is the first noninvasive access method for accurately isolating single-cell mRNA from live single cells in tissues in their natural microenvironment.

Transcriptomic encoding of sensorimotor transformation in the midbrain

Sensorimotor transformation, a process that converts sensory stimuli into motor actions, is critical for the brain to initiate behaviors. Although the circuitry involved in sensorimotor transformation has been well delineated, the molecular logic behind this process remains poorly understood. Here, we performed high-throughput and circuit-specific single-cell transcriptomic analyses of neurons in the superior colliculus (SC), a midbrain structure implicated in early sensorimotor transformation. We found that SC neurons in distinct laminae expressed discrete marker genes. Of particular interest, Cbln2 and Pitx2 were key markers that define glutamatergic projection neurons in the optic nerve (Op) and intermediate gray (InG) layers, respectively. The Cbln2+ neurons responded to visual stimuli mimicking cruising predators, while the Pitx2+ neurons encoded prey-derived vibrissal tactile cues. By forming distinct input and output connections with other brain areas, these neuronal subtypes independently mediated behaviors of predator avoidance and prey capture. Our results reveal that, in the midbrain, sensorimotor transformation for different behaviors may be performed by separate circuit modules that are molecularly defined by distinct transcriptomic codes.

Location Matters: Navigating Regional Heterogeneity of the Neurovascular Unit

The neurovascular unit (NVU) of the brain is composed of multiple cell types that act synergistically to modify blood flow to locally match the energy demand of neural activity, as well as to maintain the integrity of the blood-brain barrier (BBB). It is becoming increasingly recognized that the functional specialization, as well as the cellular composition of the NVU varies spatially. This heterogeneity is encountered as variations in vascular and perivascular cells along the arteriole-capillary-venule axis, as well as through differences in NVU composition throughout anatomical regions of the brain. Given the wide variations in metabolic demands between brain regions, especially those of gray vs. white matter, the spatial heterogeneity of the NVU is critical to brain function. Here we review recent evidence demonstrating regional specialization of the NVU between brain regions, by focusing on the heterogeneity of its individual cellular components and briefly discussing novel approaches to investigate NVU diversity.

Modeling Neurological Disorders in 3D Organoids Using Human-Derived Pluripotent Stem Cells

Modeling neurological disorders is challenging because they often have both endogenous and exogenous causes. Brain organoids consist of three-dimensional (3D) self-organizing brain tissue which increasingly is being used to model various aspects of brain development and disorders, such as the generation of neurons, neuronal migration, and functional networks. These organoids have been recognized as important in vitro tools to model developmental features of the brain, including neurological disorders, which can provide insights into the molecular mechanisms involved in those disorders. In this review, we describe recent advances in the generation of two-dimensional (2D), 3D, and blood-brain barrier models that were derived from induced pluripotent stem cells (iPSCs) and we discuss their advantages and limitations in modeling diseases, as well as explore the development of a vascularized and functional 3D model of brain processes. This review also examines the applications of brain organoids for modeling major neurodegenerative diseases and neurodevelopmental disorders.

Patch-seq: Past, Present, and Future

Single-cell transcriptomic approaches are revolutionizing neuroscience. Integrating this wealth of data with morphology and physiology, for the comprehensive study of neuronal biology, requires multiplexing gene expression data with complementary techniques. To meet this need, multiple groups in parallel have developed "Patch-seq," a modification of whole-cell patch-clamp protocols that enables mRNA sequencing of cell contents after electrophysiological recordings from individual neurons and morphologic reconstruction of the same cells. In this review, we first outline the critical technical developments that enabled robust Patch-seq experimental efforts and analytical solutions to interpret the rich multimodal data generated. We then review recent applications of Patch-seq that address novel and long-standing questions in neuroscience. These include the following: (1) targeted study of specific neuronal populations based on their anatomic location, functional properties, lineage, or a combination of these factors; (2) the compilation and integration of multimodal cell type atlases; and (3) the investigation of the molecular basis of morphologic and functional diversity. Finally, we highlight potential opportunities for further technical development and lines of research that may benefit from implementing the Patch-seq technique. As a multimodal approach at the intersection of molecular neurobiology and physiology, Patch-seq is uniquely positioned to directly link gene expression to brain function.