Thousands Of Cells Research Articles

Type 1 diabetes in the pancreas: A histological perspective.

This review aims to introduce research in the pancreas to a broader audience. The pancreas is a heterocrine gland residing deep within our abdominal cavity. It is the home to our islets, which play a pivotal role in regulating metabolic homeostasis. Due to its structure and location, it is an impossible organ to study, in molecular detail, in living humans, and yet, understanding the pancreas is critical if we aim to characterise the immunopathology of type 1 diabetes (T1D) and one day prevent the triggering of the autoimmune attack associated with ß-cell demise. Over a 100 years ago, we began studying pancreatic histology using cadaveric samples and clever adaptations to microscopes. As histologists, some may say nothing much has changed. Nevertheless, our microscopes can now interrogate multiple proteins at molecular resolution. Images of pancreas sections are no longer constrained to a single field of view and can capture a thousands and thousands of cells. AI-image-analysis packages can analyse these massive data sets offering breakthrough findings. This narrative review will provide an overview of pancreatic anatomy, and the importance of research focused on the pancreas in T1D. It will range from histological breakthroughs to briefly discussing the challenges associated with characterising the organ. I shall briefly introduce a selection of the available global biobanks and touch on the distinct pancreatic endotypes that differ immunologically and in ß-cell behaviour. Finally, I will introduce the idea of developing a collaborative tool aimed at developing a cohesive framework for characterising heterogeneity and stratifying endotypes in T1D more readily.

Single-Molecule Spatial Transcriptomics of Human Thoracic Aortic Aneurysms Uncovers Calcification-Related CARTPT-Expressing Smooth Muscle Cells.

Although single-cell RNA-sequencing is commonly applied to dissect the heterogeneity in human tissues, it involves the preparation of single-cell suspensions via cell dissociation, causing loss of spatial information. In this study, we employed high-resolution single-cell transcriptome imaging to reveal rare smooth muscle cell (SMC) types in human thoracic aortic aneurysm (TAA) tissue samples. Single-molecule spatial distribution of transcripts from 140 genes was analyzed in fresh-frozen human TAA samples with region and sex-matched controls. In vitro studies and tissue staining were performed to examine human CART prepropeptide (CARTPT) regulation and function. We captured thousands of cells per sample including a spatially distinct CARTPT-expressing SMC subtype enriched in male TAA samples. Immunoassays confirmed human CART (cocaine- and amphetamine-regulated transcript) protein enrichment in male TAA tissue and truncated CARTPT secretion into cell culture medium. Oxidized low-density lipoprotein, a cardiovascular risk factor, induced CARTPT expression, whereas CARTPT overexpression in human aortic SMCs increased the expression of key osteochondrogenic transcription factors and reduced contractile gene expression. Recombinant human CART treatment of human SMCs further confirmed this phenotype. Alizarin red staining revealed calcium deposition in male TAA samples showing similar localization with human CART staining. Here, we demonstrate the feasibility of single-molecule imaging in uncovering rare SMC subtypes in the diseased human aorta, a difficult tissue to dissociate. We identified a spatially distinct CARTPT-expressing SMC subtype enriched in male human TAA samples. Our functional studies suggest that human CART promotes osteochondrogenic switch of aortic SMCs, potentially leading to medial calcification of the thoracic aorta.

Effect of methanol fixation on single-cell RNA sequencing of the murine dentate gyrus.

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to evaluate the transcriptomic landscape and heterogeneity of thousands of cells in parallel. However, complex study designs or the unavailability of in-house instruments require the temporal disconnection between sample preparation and library construction, raising the need for efficient sample preservation methods which are compatible with scRNA-seq downstream analysis. Several studies evaluated the effect of methanol fixation as preservation method, yet none of them deeply assessed its effect on adult primary dissociated brain tissue. Here, we evaluated its effect on murine dentate gyrus (DG) single cell suspensions and on subsequent scRNA-seq downstream analysis by performing SOrting and Robot-assisted Transcriptome SEQuencing (SORT-seq), a partially robotized version of the CEL-seq2 protocol. Our results show that MeOH fixation preserves RNA integrity and has no apparent effects on cDNA library construction. They also suggest that fixation protects from sorting-induced cell stress and increases the proportion of high-quality cells. Despite evidence of mRNA leakage in fixed cells, their relative gene expression levels correlate well with those of fresh cells and fixation does not significantly affect the variance of the dataset. Moreover, it allows the identification of all major DG cell populations, including neural precursors, granule neurons and different glial cell types, with a tendency to preserve more neurons that are underrepresented in fresh samples. Overall, our data show that MeOH fixation is suitable for preserving primary neural cells for subsequent single-cell RNA profiling, helping to overcome challenges arising from complex workflows, improve experimental flexibility and facilitate scientific collaboration.

Modal analysis of lithium-ion batteries for estimation of state of charge and state of health

The robustness and safety of battery-operated systems will become critically important as society transitions away from fossil fuels. In the foreseeable future, lithium-ion batteries will be used for high-power, high-capacity applications such as electric vehicles and renewable energy storage. In these applications, which require thousands of cells, existing battery management systems do not monitor the operating conditions of each cell. Variability in temperature and pressure can affect a cell’s state of charge (SOC) or state of health (SOH). This environmental loading coupled with cell-to-cell variability makes SOC/SOH estimation very difficult. Inaccurate measurements of SOC/SOH can reduce the lifespan of battery systems or lead to accidental overcharge and thermal runaway. We present modal analysis as a viable approach to estimate SOC/SOH for electrically cycled cells. Modal tests are performed using laser Doppler vibrometry on 10 Ah Nickel–Manganese–Cobalt pouch cells at 0% and 100% SOC across more than 30 cycles. Changes in the resonance frequencies of the cell are correlated with SOC/SOH to provide an understanding of how the mechanical properties of cell components change as a function of charge level and aging. This work demonstrates that modal analysis may be used as a tool for regular battery maintenance to improve battery safety.

Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection.

Bacterial populations are highly adaptive. They can respond to stress and survive in shifting environments. How the behaviours of individual bacteria vary during stress, however, is poorly understood. To identify and characterize rare bacterial subpopulations, technologies for single-cell transcriptional profiling have been developed. Existing approaches show some degree of limitation, for example, in terms of number of cells or transcripts that can be profiled. Due in part to these limitations, few conditions have been studied with these tools. Here we develop massively-parallel, multiplexed, microbial sequencing (M3-seq)-a single-cell RNA-sequencing platform for bacteria that pairs combinatorial cell indexing with post hoc rRNA depletion. We show that M3-seq can profile bacterial cells from different species under a range of conditions in single experiments. We then apply M3-seq to hundreds of thousands of cells, revealing rare populations and insights into bet-hedging associated with stress responses and characterizing phage infection.

Single-nucleus multiomic mapping of m6A methylomes and transcriptomes in native populations of cells with sn-m6A-CT

N6-methyladenosine (m6A) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global m6A profiling in bulk sequencing, single-cell technologies for analyzing m6A heterogeneity are not extensively established. Here, we developed single-nucleus m6A-CUT&Tag (sn-m6A-CT) for simultaneous profiling of m6A methylomes and transcriptomes within a single nucleus using mouse embryonic stem cells (mESCs). m6A-CT is capable of enriching m6A-marked RNA molecules in situ, without isolating RNAs from cells. We adapted m6A-CT to the droplet-based single-cell omics platform and demonstrated high-throughput performance in analyzing nuclei isolated from thousands of cells from various cell types. We show that sn-m6A-CT profiling is sufficient to determine cell identity and allows the generation of cell-type-specific m6A methylome landscapes from heterogeneous populations. These indicate that sn-m6A-CT provides additional dimensions to multimodal datasets and insights into epitranscriptomic landscape in defining cell fate identity and states.

Microglial morphometric analysis: so many options, so little consistency.

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.

Abstract P1185: Spatial Transcriptome Profiling In The Mouse Heart With Single-Cell Resolution

Background: The spatial organization of the diverse cells in the heart shapes biological function. For instance, during the inflammatory and fibrotic processes that are central to heart disease, many cells act mainly within their local microenvironment. Recent studies using single-cell RNA sequencing (scRNA-seq) have provided a detailed list of the cell types and states involved in development, homeostasis and in cardiovascular diseases. However, scRNA-seq requires tissue dissociation, which removes spatial information and thereby limits our ability to determine how cells interact with each other to maintain health or cause disease. As a consequence, the precise spatial organization of cell types and states in the mammalian heart remains unknown. We sought to address this knowledge gap by using multiplexed error-robust fluorescence in situ hybridization (MERFISH), a method that enables simultaneous identification and localization of single transcripts from hundreds of different genes in intact tissue sections. Methods/Results: We adapted MERFISH and developed a new single-cell image segmentation method to creating a single-cell atlas of the mouse heart. First, we designed and constructed a 128-gene MERFISH probe library including cell type marker genes and also genes covering a broad range of cardiac signaling pathways. We then optimized sample preparation, including cell boundary staining and tissue clearing. Once optimized, we used this approach to image 10 μm-thick sections from healthy mouse heart, resolving tens of thousands of cells and millions of transcripts. Our data thus constitute the first single-cell spatial transcriptome atlas of the mouse heart. Conclusion: Through our newly developed cardiac MERFISH platform, we identified tens of thousands of cells and labeled millions of individual RNA transcripts in intact sections of mouse heart. Our cellular transcriptome profile maps enable new insights into how the organization of the heart shapes its biological function.

ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq.

The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells-some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them. We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective. https://github.com/Teddy-XiongGZ/ProtoCell4P.

Cellular state landscape and herpes simplex virus type 1 infection progression are connected

Prediction, prevention and treatment of virus infections require understanding of cell-to-cell variability that leads to heterogenous disease outcomes, but the source of this heterogeneity has yet to be clarified. To study the multimodal response of single human cells to herpes simplex virus type 1 (HSV-1) infection, we mapped high-dimensional viral and cellular state spaces throughout the infection using multiplexed imaging and quantitative single-cell measurements of viral and cellular mRNAs and proteins. Here we show that the high-dimensional cellular state scape can predict heterogenous infections, and cells move through the cellular state landscape according to infection progression. Spatial information reveals that infection changes the cellular state of both infected cells and of their neighbors. The multiplexed imaging of HSV-1-induced cellular modifications links infection progression to changes in signaling responses, transcriptional activity, and processing bodies. Our data show that multiplexed quantification of responses at the single-cell level, across thousands of cells helps predict infections and identify new targets for antivirals.

An Intermodular Active Balancing Topology for Efficient Operation of High Voltage Battery Packs in Li-Ion Based Energy Storage Systems: Switched (Flying) DC/DC Converter

To meet the load voltage and power requirements for various specific needs, a typical lithium–ion battery (LIB) pack consists of different parallel and series combinations of individual cells in modules, which can go as high as tens of series and parallel connections in each module, reaching hundreds and even thousands of cells at high voltage (HV) levels. The inhomogeneity among the cells and modules results in voltage imbalances during operation and reduces the overall system efficiency. In this work, a robust and flexible active balancing topology is presented. It can not only mitigate the charge imbalance within a module, i.e., intramodular equalization, but also help to balance the state of charge (SoC) level of the modules in a high voltage pack, i.e., intermodular equalization, which is an often-overlooked topic. The proposed concept was proven by experimental verification on parallel and series configurations of cells in realistically sized modules and practical battery management system (BMS) hardware, when the LIB was both idle and under load.

CellCounts: an R function for quantifying 10xChromium single-cell RNA sequencing data.

The 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression of genes in individual cells due to the massive data volume. Here, we present cellCounts, a new tool for efficient and accurate quantification of Chromium data. cellCounts employs the seed-and-vote strategy to align reads to a reference genome, collapses reads to Unique Molecular Identifiers (UMIs) and then assigns UMIs to genes based on the featureCounts program. Using both simulation and real datasets for evaluation, cellCounts was found to compare favourably to cellRanger and STARsolo. cellCounts is implemented in R, making it easily integrated with other R programs for analysing Chromium data. cellCounts was implemented as a function in R package Rsubread that can be downloaded from http://bioconductor.org/packages/release/bioc/html/Rsubread.html. Data and analysis code used in this study can be freely accessed via La Trobe University's Institutional Repository at https://doi.org/10.26181/21588276.

Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis

Colorectal cancer is the third most common cancer and second cancer with the highest mortality rate in the world. Progression, which leads to metastasis, is one of the biggest challenges in cancer treatment, and despite improvement in screening and treatment techniques, 5 years of survival of colorectal cancer patients drop from 91% in stage I to 12% in stage IV. Single-cell RNA sequencing is one of the most powerful tools to study complex diseases such as cancer, and despite its recent emergence, it’s rapidly growing. In contrast to bulk RNA sequencing, which averages out expression of thousands of cells, single-cell RNA sequencing can capture intra-tumor heterogeneity. Moreover, cellular dynamic events like progression can be studied by pseudotime trajectory analysis of single-cell RNA sequencing data. Herein we used Samsung Medical Center (SMC) colorectal cancer single-cell RNA sequencing dataset to find important tumor epithelial cells subtypes. Subsequently, we’ve found important genes with a dynamic pattern along cancer progression by using pseudo-time trajectory analysis.Also, we found TGFB1 and IL1B as effective ligands and several transcription factors which may regulate the expression of pseudo-time related genes. In the end, we’ve constructed a LASSO cox regression using 20 psudotime genes, which can predict 3-year survival of colorectal cancer patients with AUC >0.7.

Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data

Background: Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells. Methods: This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR. Results: Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland. Conclusions: The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Abstract A057: Microscale analysis of histone modifications in rare cell populations in prostate cancer

Abstract Pre-clinical models have identified diverse epigenetic alterations in prostate cancer that associate with treatment resistance and may also be therapeutic targets. However, these assays typically require thousands of cells and are difficult to scale for patient tumor biopsies. This report seeks to adapt the Cleavage Under Targets and Tagmentation (CUT&Tag) assay for use on a microfluidic technology to eliminate cell loss associated with complex, multi-step epigenetic assays to facilitate analysis of low input primary tumor samples. Exclusive Liquid Repellant (ELR) platforms pass magnetic bead bound analytes through immiscible phases to separate and extract analytes of interest while eliminating traditional centrifugation and wash steps that lead to sample loss. This platform enables adaptation of complex molecular assays such as CUT&Tag for lossless sample manipulation. We focus on the analysis of histone modifications in rare cells utilizing antibodies for H3K27 acetylation and methylation. Comparison of prostate cancer cell lines with LNCaP, V16D, 49F, 42D, and H660s with serial dilutions was performed to evaluate the sensitivity and specificity of this integrated assay. The CUT&Tag assay was performed in standard fashion on a serial dilution of 50-100,000 cells from LNCaP and V16D calls and showed dropout of recoverable DNA at 50 cells. Sequencing of DNA at these dilutions confirmed identification of differential histone modifications consistent with previously published data. CUT&Tag was adapted to ELR through the serial patterning of each buffer in distinct wells connected by an oil interface. In order to optimize the assay for reproducibility and increased sensitivity, we optimized antibody binding/washing to minimize the protein A-Transposase inhibition. This optimization showed increased antibody binding by 36% compared to protocol defined concentrations. We have now successfully performed this assay with a sensitivity of ~100 cells with recoverable DNA for sequencing to identify heterogenous histone patterns in each cell line. CUT&Tag has improved sensitivity to detect epigenetic alterations that associate with aggressive prostate cancer. The integration of CUT&Tag with ELR improves both sensitivity and reproducibility for epigenetic analysis at low input cell numbers. This assay is now being utilized to analyze patient tumor samples and prostate cancer heterogeneity. Citation Format: Zachary J. Kauffman, Kevin Koesser, Kyle T. Helzer, Jamie M. Sperger, Marina N. Sharifi, Chao Li, Duane S. Juang, Cole S. Gilsdorf, Shuang G. Zhao, David J. Beebe, Joshua M. Lang. Microscale analysis of histone modifications in rare cell populations in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A057.

Single-cell Ca2+ parameter inference reveals how transcriptional states inform dynamic cell responses.

Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca2+ dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca2+ signalling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca2+ dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signalling dynamics in single cells.

Mapping interindividual dynamics of innate immune response at single-cell resolution

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

ScSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data

BackgroundSingle-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging.ResultsWe propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses.ConclusionscSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE.

Following nanoparticle uptake by cells using high-throughput microscopy and the deep-learning based cell identification algorithm Cellpose

How many nanoparticles are taken up by human cells is a key question for many applications, both within medicine and safety. While many methods have been developed and applied to this question, microscopy-based methods present some unique advantages. However, the laborious nature of microscopy, in particular the consequent image analysis, remains a bottleneck. Automated image analysis has been pursued to remedy this situation, but offers its own challenges. Here we tested the recently developed deep-learning based cell identification algorithm Cellpose on fluorescence microscopy images of HeLa cells. We found that the algorithm performed very well, and hence developed a workflow that allowed us to acquire, and analyse, thousands of cells in a relatively modest amount of time, without sacrificing cell identification accuracy. We subsequently tested the workflow on images of cells exposed to fluorescently-labelled polystyrene nanoparticles. This dataset was then used to study the relationship between cell size and nanoparticle uptake, a subject where high-throughput microscopy is of particular utility.

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease.

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencingtechnologies.