Cellpose: a generalist algorithm for cellular segmentation

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

A generative benchmark for evaluating the performance of fluorescent cell image segmentation

Fluorescent cell imaging technology is fundamental in life science research, offering a rich source of image data crucial for understanding cell spatial positioning, differentiation, and decision-making mechanisms. As the volume of this data expands, precise image analysis becomes increasingly critical. Cell segmentation, a key analysis step, significantly influences quantitative analysis outcomes. However, selecting the most effective segmentation method is challenging, hindered by existing evaluation methods' inaccuracies, lack of graded evaluation, and narrow assessment scope. Addressing this, we developed a novel framework with two modules: StyleGAN2-based contour generation and Pix2PixHD-based image rendering, producing diverse, graded-density cell images. Using this dataset, we evaluated three leading cell segmentation methods: DeepCell, CellProfiler, and CellPose. Our comprehensive comparison revealed CellProfiler's superior accuracy in segmenting cytoplasm and nuclei. Our framework diversifies cell image data generation and systematically addresses evaluation challenges in cell segmentation technologies, establishing a solid foundation for advancing research and applications in cell image analysis.

NeuroQuantify - An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning

BACKGROUND

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals.

NEW METHOD

We have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images.

RESULTS

NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations.

COMPARISON WITH EXISTING METHODS

NeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution.

CONCLUSIONS

We offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation.

Remodeling of the secretory pathway is coordinated with de novo membrane formation in budding yeast gametogenesis

Gametogenesis in budding yeast involves a large-scale rearrangement of membrane traffic to allow the de novo formation of a membrane, called the prospore membrane (PSM). However, the mechanism underlying this event is not fully elucidated. Here, we show that the number of endoplasmic reticulum exit sites (ERES) per cell fluctuates and switches from decreasing to increasing upon the onset of PSM formation. Reduction in ERES number, presumably accompanying a transient stall in membrane traffic, resulting in the loss of preexisting Golgi apparatus from the cell, was followed by local ERES regeneration, leading to Golgi reassembly in nascent spores. We have revealed that protein phosphatase-1 (PP-1) and its development-specific subunit, Gip1, promote ERES regeneration through Sec16 foci formation. Furthermore, sed4Δ, a mutant with impaired ERES formation, showed defects in PSM growth and spore formation. Thus, ERES regeneration in nascent spores facilitates the segregation of membrane traffic organelles, leading to PSM growth.

Dynamics of RNA localization to nuclear speckles are connected to splicing efficiency

Nuclear speckles are nuclear membraneless organelles in higher eukaryotic cells playing a vital role in gene expression. Using an in situ reverse transcription-based sequencing method, we study nuclear speckle-associated human transcripts. Our data indicate the existence of three gene groups whose transcripts demonstrate different speckle localization properties: stably enriched in nuclear speckles, transiently enriched in speckles at the pre-messenger RNA stage, and not enriched. We find that stably enriched transcripts contain inefficiently excised introns and that disruption of nuclear speckles specifically affects splicing of speckle-enriched transcripts. We further reveal RNA sequence features contributing to transcript speckle localization, indicating a tight interplay between transcript speckle enrichment, genome organization, and splicing efficiency. Collectively, our data highlight a role of nuclear speckles in both co- and posttranscriptional splicing regulation. Last, we show that genes with stably enriched transcripts are over-represented among genes with heat shock-up-regulated intron retention, hinting at a connection between speckle localization and cellular stress response.

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

Heterogeneous force response of chromatin in isolated nuclei

A quantitative description of nuclear mechanics is crucial for understanding its role in force sensing within eukaryotic cells. Recent studies indicate that the chromatin within the nucleus cannot be treated as a homogeneous material. To elucidate its material properties, we combine optical tweezers manipulation of isolated nuclei with multi-color fluorescence imaging of lamin and chromatin to map the response of nuclei to local deformations. Force spectroscopy reveals nuclear strain stiffening and an exponential force dependence, well described by a hierarchical chain model. Simultaneously, fluorescence data show a higher compliance of chromatin compared to the nuclear envelope at strains <30%. Micrococcal nuclease (MNase) digestion of chromatin results in nuclear softening and can be captured by our model. Additionally, we observe stretching responses showing a lipid tether signature, suggesting that these tethers originate from the nuclear membrane. Our combined approach allows us to elucidate the nuclear force response while mapping the deformation of lamin, (eu)chromatin, and membrane.

Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation

The regulation of gene expression in eukaryotes relies largely on the action of exoribonucleases, evolutionarily conserved enzymes that digest decapped messenger RNAs in the 5'-3' direction. The activity of Xrn1, the major yeast exoribonuclease, is regulated by targeted changes in its cellular localisation in direct response to the cell's metabolic state. When fermentable carbon sources are available, active Xrn1 is diffusely localised in the cytosol. Upon depletion of these sources, Xrn1 is sequestered at the plasma membrane-associated protein complex, the eisosome, and becomes inactive. Although this phenomenon has been described previously, the molecular mechanisms underlying these changes remain unknown. We report that the binding of Xrn1 to the plasma membrane is subject to glycolytic flux, rather than the availability of a fermentable carbon source, is independent of TORC1 activity and requires the core eisosomal proteins Pil1 and Lsp1. We identify the SH3-like domain of the Xrn1 protein as a putative interaction domain. In addition, we show that when expressed in Saccharomyces cerevisiae, the human orthologue of Xrn1 mirrors its yeast counterpart, i.e., it segregates to the eisosome under conditions of halted glycolysis. Our results not only advance our understanding of Xrn1 regulation but also indicate that this regulatory principle is conserved from yeast to humans.

cellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Reconstructing signaling history of single cells with imaging-based molecular recording

The intensity and duration of biological signals encode information that allows a few pathways to regulate a wide array of cellular behaviors. Despite the central importance of signaling in biomedical research, our ability to quantify it in individual cells over time remains limited. Here, we introduce INSCRIBE, an approach for reconstructing signaling history in single cells using endpoint fluorescence images. By regulating a CRISPR base editor, INSCRIBE generates mutations in genomic target sequences, at a rate proportional to signaling activity. The number of edits is then recovered through a novel ratiometric readout strategy, from images of two fluorescence channels. We engineered human cell lines for recording WNT and BMP pathway activity, and demonstrated that INSCRIBE faithfully recovers both the intensity and duration of signaling. Further, we used INSCRIBE to study the variability of cellular response to WNT and BMP stimulation, and test whether the magnitude of response is a stable, heritable trait. We found a persistent memory in the BMP pathway. Progeny of cells with higher BMP response levels are likely to respond more strongly to a second BMP stimulation, up to 3 weeks later. Together, our results establish a scalable platform for genetic recording and in situ readout of signaling history in single cells, advancing quantitative analysis of cell-cell communication during development and disease.

Loss of atrial natriuretic peptide signaling causes insulin resistance, mitochondrial dysfunction, and low endurance capacity

Type 2 diabetes (T2D) and obesity are strongly associated with low natriuretic peptide (NP) plasma levels and a down-regulation of NP guanylyl cyclase receptor-A (GCA) in skeletal muscle and adipose tissue. However, no study has so far provided evidence for a causal link between atrial NP (ANP)/GCA deficiency and T2D pathogenesis. Here, we show that both systemic and skeletal muscle ANP/GCA deficiencies in mice promote metabolic disturbances and prediabetes. Skeletal muscle insulin resistance is further associated with altered mitochondrial function and impaired endurance running capacity. ANP/GCA-deficient mice exhibit increased proton leak and reduced content of mitochondrial oxidative phosphorylation proteins. We further show that GCA is related to several metabolic traits in T2D and positively correlates with markers of oxidative capacity in human skeletal muscle. Together, these results indicate that ANP/GCA signaling controls muscle mitochondrial integrity and oxidative capacity in vivo and plays a causal role in the development of prediabetes.

Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen

The mechanisms that guide T helper 2 (TH2) cell differentiation in barrier tissues are unclear. Here we describe the molecular pathways driving allergen-specific TH2 cells using temporal, spatial and single-cell transcriptomic tracking of house dust mite-specific T cells in mice. Differentiation and migration of lung allergen-specific TH2 cells requires early expression of the transcriptional repressor Blimp-1. Loss of Blimp-1 during priming in the lymph node ablated the formation of TH2 cells in the lung, indicating early Blimp-1 promotes TH2 cells with migratory capability. IL-2/STAT5 signals and autocrine/paracrine IL-10 from house dust mite-specific T cells were essential for Blimp-1 and subsequent GATA3 upregulation through repression of Bcl6 and Bach2. Spatial microniches of IL-2 in the lymph node supported the earliest Blimp-1+TH2 cells, demonstrating lymph node localization is a driver of TH2 initiation. Our findings identify an early requirement for IL-2-mediated spatial microniches that integrate with allergen-driven IL-10 from responding T cells to drive allergic asthma.

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell-mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.

Polarized exocytosis and anionic phospholipid species implicated in the initiation of clathrin-mediated endocytosis

Understanding of the mechanisms that initiate clathrin-mediated endocytosis (CME) is incomplete. Recent studies in budding yeast identified the endocytic adaptor protein Yap1801/Yap1802 (budding yeast AP180) as a key CME factor that promotes CME initiation in daughter cells during polarized growth, but how Yap1801/2 is recruited preferentially to the plasma membrane of daughter cells is not clear. The only known cargos for Yap1801/2 in yeast are the synaptobrevins Snc1 and Snc2, which act as v-SNARES for exocytic vesicles arriving at the plasma membrane and are essential for polarized cell growth. In this study, we analyze the spatiotemporal dynamics of functional, fluorescently-tagged Snc1/2 expressed from their endogenous loci and provide evidence that, in concert with anionic phospholipids, Snc1/2 recruit Yap1801/2 preferentially to growing daughter cells. These findings suggest that the coincidence of anionic phospholipids and Snc1/2 facilitates CME initiation in growing daughter cells and directly links polarized CME to polarized secretion.

Introducing FISCAS, a Tool for the Effective Generation of Single Cell MALDI-MSI Data

We introduce Fluorescence Integrated Single-Cell Analysis Script (FISCAS), which combines fluorescence microscopy with MALDI-MSI to streamline single-cell analysis. FISCAS enables automated selection of tight measurement regions, thereby reducing the acquisition of off-target pixels, and makes use of established algorithms for cell segmentation and coregistration to rapidly compile single-cell spectra. MALDI-compatible staining of membranes, nuclei, and lipid droplets allows the collection of fluorescence data prior to the MALDI-MSI measurement on a timsTOF fleX MALDI-2. Usefulness of the software is demonstrated by the example of THP-1 cells during stimulated differentiation into macrophages at different time points. In this proof-of-principle study, FISCAS was used to automatically generate single-cell mass spectra along with a wide range of morphometric parameters for a total number of roughly 1300 cells collected at 24, 48, and 72 h after the onset of stimulation. Data analysis of the combined morphometric and single-cell mass spectrometry data shows significant molecular heterogeneity within the cell population at each time point, indicating an independent differentiation of each individual cell rather than a synchronized mechanism. Here, the grouping of cells based on their molecular phenotype revealed an overall clearer distinction of the different phases of differentiation into macrophages and delivered an increased number of lipid signals as possible markers compared with traditional bulk analysis. Utilizing the linkage between mass spectrometric data and fluorescence microscopy confirmed the expected positive correlation between lipid droplet staining and the overall signal for triacylglyceride (TG), demonstrating the usefulness of this multimodal approach.

Rapid identification of bacterial isolates using microfluidic adaptive channels and multiplexed fluorescence microscopy

We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70-98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.

Exploring neuronal markers and early social environment influence in divergent quail lines selected for social motivation

Many species, including humans exhibit a wide range of social behaviors that are crucial for the adaptation and survival of most species. Brain organization and function are shaped by genetic and environmental factors, although their precise contributions have been relatively understudied in the context of artificial selection. We used divergent lines of quail selected on their high versus low level of motivation to approach a group of conspecifics (S + and S-, respectively) to investigate the influence of genetic selection and early social environment on sociability. We observed distinct sex- and brain-region-specific expression patterns of three neuronal markers: mesotocin, and vasotocin, the avian homologues of mammalian oxytocin and vasopressin, as well as aromatase, the enzyme that converts androgens into estrogens. These markers displayed pronounced and neuroanatomically specific differences between S + and S- quail. Additionally, in a second experiment, we assessed the influence of early social environment on social skills in juvenile birds. Mixing S + and S- resulted in more S- males approaching the group without affecting the sociability of S + or other behaviors, suggesting that the early social environment may influence the results of genetic selection. In conclusion, the divergent quail lines offer a valuable model for unraveling the neuronal and behavioral mechanisms underlying social behaviors.

Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains

Genetic variation can alter brain structure and, consequently, function. Comparative statistical analysis of mouse brains across genetic backgrounds requires spatial, single-cell, atlas-scale data, in replicates-a challenge for current technologies. We introduce A tlas-scale T ranscriptome L ocalization using A ggregate S ignatures (ATLAS), a scalable tissue mapping method. ATLAS learns transcriptional signatures from scRNAseq data, encodes them in situ with tens of thousands of oligonucleotide probes, and decodes them to infer cell types and imputed transcriptomes. We validated ATLAS by comparing its cell type inferences with direct MERFISH measurements of marker genes and quantitative comparisons to four other technologies. Using ATLAS, we mapped the central brains of five male and five female C57BL/6J (B6) mice and five male BTBR T+ tf/J (BTBR) mice, an idiopathic model of autism, collectively profiling over 40 million cells across over 400 coronal sections. Our analysis revealed over 40 significant differences in cell type distributions and identified 16 regional composition changes across male-female and B6-BTBR comparisons. ATLAS thus enables systematic comparative studies, facilitating organ-level structure-function analysis of disease models.

Microangiopathy in temporal lobe epilepsy with diffusion MRI alterations and cognitive decline

White matter microvascular alterations in temporal lobe epilepsy (TLE) may be relevant to acquired neurodegenerative processes and cognitive impairments associated with this condition. We quantified microvascular changes, myelin, axonal, glial and extracellular-matrix labelling in the gyral core and deep temporal lobe white matter regions in surgical resections from 44 TLE patients with or without hippocampal sclerosis. We compared this pathology data with in vivo pre-operative MRI diffusion measurements in co-registered regions and neuropsychological measures of cognitive impairment and decline. In resections, increased arteriolosclerosis was observed in TLE compared to non-epilepsy controls (greater sclerotic index, p < 0.001), independent of age. Microvascular changes included increased vascular densities in some regions but uniformly reduced mean vascular size (quantified with collagen-4, p < 0.05-0.0001), and increased pericyte coverage of small vessels and capillaries particularly in deep white matter (quantified with platelet-derived growth factor receptorβ and smooth muscle actin, p < 0.01) which was more marked the longer the duration of epilepsy (p < 0.05). We noted increased glial numbers (Olig2, Iba1) but reduced myelin (MAG, PLP) in TLE compared to controls, particularly prominent in deep white matter. Gene expression analysis showed a greater reduction of myelination genes in HS than non-HS cases and with age and correlation with diffusion MRI alterations. Glial densities and vascular size were increased with increased MRI diffusivity and vascular density with white matter abnormality quantified using fixel-based analysis. Increased perivascular space was associated with reduced fractional anisotropy as well as age-accelerated cognitive decline prior to surgery (p < 0.05). In summary, likely acquired microangiopathic changes in TLE, including vascular sclerosis, increased pericyte coverage and reduced small vessel size, may indicate a functional alteration in contractility of small vessels and haemodynamics that could impact on tissue perfusion. These morphological features correlate with white matter diffusion MRI alterations and might explain cognitive decline in TLE.

Single cell spatial profiling of FFPE splenic tissue from a humanized mouse model of HIV infection

BACKGROUND

Latency remains a major obstacle to finding a cure for HIV despite the availability of antiretroviral therapy. Due to virus dormancy, limited biomarkers are available to identify latent HIV-infected cells. Profiling of individual HIV-infected cells is needed to explore potential latency biomarkers and to study the mechanisms of persistence that maintain the HIV reservoir.

METHODS

Single cell spatial transcriptomic characterization using the CosMx Spatial Molecular Imager platform was conducted to analyze HIV-infected cells in formalin-fixed paraffin-embedded sections of splenic tissue surgically obtained from an HIV-infected humanized mouse model. Regulation of over a thousand human genes was quantified in both viremic and aviremic specimens. In addition, in situ hybridization and immunohistochemistry were performed in parallel to identify HIV viral RNA- and p24-containing cells, respectively. Finally, initial findings from CosMx gene profiling were confirmed by isolating RNA from CD4 + T cells obtained from a person living with HIV on antiretroviral therapy following either PMA/Ionomycin or DMSO treatment. RNA was quantified using qPCR for a panel of targeted human host genes.

RESULTS

Supervised cell typing revealed that most of the HIV-infected cells in the mouse spleen sections were differentiated CD4 + T cells. A significantly higher number of infected cells, 2781 (1.61%) in comparison to 112 (0.06%), and total HIV transcripts per infected cell were observed in viremic samples compared to aviremic samples, respectively, which was consistent with the data obtained from ISH and IHC. Notably, the expression of 55 genes was different in infected cells within tissue from aviremic animals compared to viremic. In particular, both spleen tyrosine kinase (SYK) and CXCL17, were expressed approximately 100-fold higher. This data was further evaluated against bulk RNA isolated from HIV-infected human primary CD4 + T cells. A nearly 6-fold higher expression of SYK mRNA was observed in DMSO-treated CD4 + T cells compared to those stimulated with PMA/Ionomycin.

CONCLUSION

This study found that the CosMx SMI platform is valuable for assessing HIV infection and providing insights into host biomarkers associated with HIV reservoirs. Higher relative expression of the SYK gene in aviremic-infected cells from the humanized mouse HIV model was consistent with levels found in CD4 + T cells of aviremic donors.

Mapping multimodal phenotypes to perturbations in cells and tissue with CRISPRmap

Unlike sequencing-based methods, which require cell lysis, optical pooled genetic screens enable investigation of spatial phenotypes, including cell morphology, protein subcellular localization, cell-cell interactions and tissue organization, in response to targeted CRISPR perturbations. Here we report a multimodal optical pooled CRISPR screening method, which we call CRISPRmap. CRISPRmap combines in situ CRISPR guide-identifying barcode readout with multiplexed immunofluorescence and RNA detection. Barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency. CRISPRmap enables in situ barcode readout in cell types and contexts that were elusive to conventional optical pooled screening, including cultured primary cells, embryonic stem cells, induced pluripotent stem cells, derived neurons and in vivo cells in a tissue context. We conducted a screen in a breast cancer cell line of the effects of DNA damage repair gene variants on cellular responses to commonly used cancer therapies, and we show that optical phenotyping pinpoints likely pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Angiomotin cleavage promotes leader formation and collective cell migration

Collective cell migration (CCM) is involved in multiple biological processes, including embryonic morphogenesis, angiogenesis, and cancer invasion. However, the molecular mechanisms underlying CCM, especially leader cell formation, are poorly understood. Here, we show that a signaling pathway regulating angiomotin (AMOT) cleavage plays a role in CCM, using mammalian epithelial cells and mouse models. In a confluent epithelial monolayer, full-length AMOT localizes at cell-cell junctions and limits cell motility. After cleavage, the C-terminal fragment of AMOT (AMOT-CT) translocates to the cell-matrix interface to promote the maturation of focal adhesions (FAs), generate traction force, and induce leader cell formation. Meanwhile, decreased full-length AMOT at cell-cell junctions leads to tissue fluidization and coherent migration of cell collectives. Hence, the cleavage of AMOT serves as a molecular switch to generate polarized contraction, promoting leader cell formation and CCM.

Optogenetic estimation of synaptic connections in brain slices

BACKGROUND

Detection of synaptic connections is essential for understanding neural circuits. By using optogenetics, current injection, and glutamate uncaging to activate presynaptic cells and simultaneously recording the subsequent response of postsynaptic cells, the presence of synaptic connections can be confirmed. However, these methods present throughput challenges, such as the need for simultaneous multicellular patch-clamp recording and two-photon microscopy. These challenges lead to a trade-off between sacrificing resolution and experimental throughput.

NEW METHOD

We adopted the laser, typically used for local field ablation, and combined this with post hoc analysis. We successfully approximated the synaptic connection probabilities using only an epi-fluorescence microscope and single-cell recordings.

RESULTS

We sequentially stimulated the channelrhodopsin 2-expressing cells surrounding the recorded cell and approximated the synaptic connection probabilities. This probability value was comparable to that obtained from simultaneous multi-cell patch-clamp recordings, which included more than 600 pairs.

COMPARISON WITH EXISTING METHODS

Our setup allows us to estimate connection probabilities within 100 s, outperforming existing methods. We successfully estimated synaptic connection probabilities using only the optical path typically used by an epi-fluorescence microscope and single-cell recordings. It may also be suitable for dendritic ablation experiments.

CONCLUSIONS

The proposed method simplifies the estimation of connection probabilities, which is expected to advance the study of neural circuits in conditions such as autism and schizophrenia where connection probabilities vary. Furthermore, this approach is applicable not only to local circuits but also to long-range connections, thus increasing experimental throughput.

Ergosterol promotes aggregation of natamycin in the yeast plasma membrane

Polyene macrolides are antifungal substances, which interact with cells in a sterol-dependent manner. While being widely used, their mode of action is poorly understood. Here, we employ ultraviolet-sensitive (UV) microscopy to show that the antifungal polyene natamycin binds to the yeast plasma membrane (PM) and causes permeation of propidium iodide into cells. Right before membrane permeability became compromised, we observed clustering of natamycin in the PM that was independent of PM protein domains. Aggregation of natamycin was paralleled by cell deformation and membrane blebbing as revealed by soft X-ray microscopy. Substituting ergosterol for cholesterol decreased natamycin binding and caused a reduced clustering of natamycin in the PM. Blocking of ergosterol synthesis necessitates sterol import via the ABC transporters Aus1/Pdr11 to ensure natamycin binding. Quantitative imaging of dehydroergosterol (DHE) and cholestatrienol (CTL), two analogues of ergosterol and cholesterol, respectively, revealed a largely homogeneous lateral sterol distribution in the PM, ruling out that natamycin binds to pre-assembled sterol domains. Depletion of sphingolipids using myriocin increased natamycin binding to yeast cells, likely by increasing the ergosterol fraction in the outer PM leaflet. Importantly, binding and membrane aggregation of natamycin was paralleled by a decrease of the dipole potential in the PM, and this effect was enhanced in the presence of myriocin. We conclude that ergosterol promotes binding and aggregation of natamycin in the yeast PM, which can be synergistically enhanced by inhibitors of sphingolipid synthesis.

A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2'-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical.

All-optical voltage imaging-guided postsynaptic single-cell transcriptome profiling with Voltage-Seq

Neuronal pathways recruit large postsynaptic populations and maintain connections via distinct postsynaptic response types (PRTs). Until recently, PRTs were accessible as a selection criterion for single-cell RNA sequencing only through probing by low-throughput whole-cell electrophysiology. To overcome these limitations and target neurons on the basis of specific PRTs for soma collection and subsequent single-cell RNA sequencing, we developed Voltage-Seq using the genetically encoded voltage indicator Voltron in acute brain slices from mice. We also created an onsite analysis tool, VoltView, to guide soma collection of specific PRTs using a classifier based on a previously acquired database of connectomes from multiple animals. Here we present our procedure for preparing the optical path, the imaging setup and detailing the imaging and analysis steps, as well as a complete procedure for sequencing library preparation. This enables researchers to conduct our high-throughput all-optical synaptic assay and to obtain single-cell transcriptomic data from selected postsynaptic neurons. This also allows researchers to resolve the connectivity ratio of a specific pathway and explore the diversity of PRTs within that connectome. Furthermore, combining high throughput with quick analysis gives unique access to find specific connections within a large postsynaptic connectome. Voltage-Seq also allows the investigation of correlations between connectivity and gene expression changes in a postsynaptic cell-type-specific manner for both excitatory and inhibitory connections. The Voltage-Seq workflow can be completed in ~6 weeks, including 4-5 weeks for viral expression of the Voltron sensor. The technique requires knowledge of basic laboratory techniques, micromanipulator handling skills and experience in molecular biology and bioinformatics.

A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging

Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM).

MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination

The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error prone and subjective. Several software tools have been proposed for addressing these limitations but suffer from various drawbacks, including unintuitive interfaces and limited performance. In this study, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared with other tools, MyoFInDer stands out as it minimizes the interoperator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.

Cell dynamics revealed by microscopy advances

Cell biology emerges from spatiotemporally coordinated molecular processes. Recent advances in live-cell microscopy, fueled by a surge in optical, molecular, and computational technologies, have enabled dynamic observations from single molecules to whole organisms. Despite technological leaps, there is still an untapped opportunity to fully leverage their capabilities toward biological insight. We highlight how single-molecule imaging has transformed our understanding of biological processes, with a focus on chromatin organization and transcription in the nucleus. We describe how this was enabled by the close integration of new imaging techniques with analysis tools and discuss the challenges to make a comparable impact at larger scales from organelles to organisms. By highlighting recent successful examples, we describe an outlook of ever-increasing data and the need for seamless integration between dataset visualization and quantification to realize the full potential warranted by advances in new imaging technologies.

Ladinin-1 in actin arcs of oral squamous cell carcinoma is involved in cell migration and epithelial phenotype

Histopathologically, oral squamous cell carcinoma (OSCC) consists of well-defined interfaces with adjacent non-cancerous epithelium. Previously, we found that SCC tissues expressed higher levels of specific proteins at this interface. Ladinin-1 (LAD1) is one of the specific molecules that has increased expressions in cancer fronts; however, its function in OSCC is unknown. Therefore, this study aimed to elucidate the function of LAD1 in human OSCC cells. LAD1 was localized on the actin arc at the distal periphery of cell clusters in the OSCC cell lines HSC-2, HSC-3, and HSC-4. When LAD1 was knocked down, cellular migration was repressed in wound scratch assays but was reversed in three-dimensional collagen gel invasion assays. Characteristic LAD1 localization along actin arcs forming the leading edge of migrating cells was diminished with loss of filopodia formation and ruffling in knockdown cells, in which the expression levels of cell motility-related genes-p21-activated kinase 1 (PAK1) and caveolin-1 (CAV1)-were upregulated and downregulated, respectively. LAD1 expression was also associated with the downregulation of vimentin and increased histological differentiation of OSCC. These results suggest that LAD1 is involved in actin dynamics during filopodia and lamellipodia formation, and in maintaining the epithelial phenotype of OSCC cells.

Mapping cellular interactions from spatially resolved transcriptomics data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.

Restricting the level of the proteins essential for the regulation of the initiation step of replication extends the chronological lifespan and reproductive potential in budding yeast

Aging is defined as a progressive decline in physiological integrity, leading to impaired biological function, including fertility, and rising vulnerability to death. Disorders of DNA replication often lead to replication stress and are identified as factors influencing the aging rate. In this study, we aimed to reveal how the cells that lost strict control of the formation of crucial for replication initiation a pre-initiation complex impact the cells' physiology and aging. As strains with the lower pre-IC control (lowPICC) we used, Saccharomyces cerevisiae heterozygous strains having only one functional copy of genes, encoding essential replication proteins such as Cdc6, Dbf4, Sld3, Sld7, Sld2, and Mcm10. The lowPICC strains exhibited a significant reduction in the respective genes' mRNA levels, causing cell cycle aberrations and doubling time extensions. Additionally, the reduced expression of the lowPICC genes led to an aberrant DNA damage response, affected cellular and mitochondrial DNA content, extended the lifespan of post-mitotic cells, and increased the yeast's reproductive potential. Importantly, we also demonstrated a strong negative correlation between the content of cellular macromolecules (RNA, proteins, lipids, polysaccharides) and aging. The data presented here will likely contribute to the future development of therapies for treating various human diseases.

Fat body glycolysis defects inhibit mTOR and promote distant muscle disorganization through TNF-α/egr and ImpL2 signaling in Drosophila larvae

The fat body in Drosophila larvae functions as a reserve tissue and participates in the regulation of organismal growth and homeostasis through its endocrine activity. To better understand its role in growth coordination, we induced fat body atrophy by knocking down several key enzymes of the glycolytic pathway in adipose cells. Our results show that impairing the last steps of glycolysis leads to a drastic drop in adipose cell size and lipid droplet content, and downregulation of the mTOR pathway and REPTOR transcriptional activity. Strikingly, fat body atrophy results in the distant disorganization of body wall muscles and the release of muscle-specific proteins in the hemolymph. Furthermore, we showed that REPTOR activity is required for fat body atrophy downstream of glycolysis inhibition, and that the effect of fat body atrophy on muscles depends on the production of TNF-α/egr and of the insulin pathway inhibitor ImpL2.

Spatial omics technologies for understanding molecular status associated with cancer progression

Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.

HepG2 cells undergo regulatory volume decrease by mechanically induced efflux of water and solutes

This study challenges the conventional belief that animal cell membranes lack a significant hydrostatic gradient, particularly under anisotonic conditions, as demonstrated in the human hepatoma cell line HepG2. The Boyle van't Hoff (BvH) relation describes volumetric equilibration to anisotonic conditions for many cells. However, the BvH relation is simple and does not include many cellular components such as the cytoskeleton and actin cortex, mechanosensitive channels, and ion pumps. Here we present alternative models that account for mechanical resistance to volumetric expansion, solute leakage, and active ion pumping. We found the BvH relation works well to describe hypertonic volume equilibration but not hypotonic volume equilibration. After anisotonic exposure and return isotonic conditions cell volumes were smaller than their initial isotonic volume, indicating solutes had leaked out of the cell during swelling. Finally, we observed HepG2 cells undergo regulatory volume decrease at both 20 °C and 4 °C, indicating regulatory volume decrease to be a relatively passive phenomenon and not driven by ion pumps. We determined the turgor-leak model, which accounts for mechanical resistance and solute leakage, best fits the observations found in the suite of experiments performed, while other models were rejected.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

Spatial resolution of the head and neck cancer tumor microenvironment to identify tumor and stromal features associated with therapy response

Head and neck cancer (HNC) is the seventh most common cancer globally, resulting in 440 000 deaths per year. While there have been advancements in chemoradiotherapy and surgery, relapse occurs in more than half of HNCs, and these patients have a median survival of 10 months and a 2-year survival of < 20%. Only a subset of patients displays durable benefits from immunotherapies in metastatic and recurrent HNC, making it critical to understand the tumor microenvironment (TME) underpinning therapy responses in HNC. To recognize biological differences within the TME that may be predictive of immunotherapy response, we applied cutting-edge geospatial whole-transcriptome profiling (NanoString GeoMx Digital Spatial Profiler) and spatial proteomics profiling (Akoya PhenoCycler-Fusion) on a tumor microarray consisting of 25 cores from 12 patients that included 4 immunotherapy-unresponsive (8 cores) and 2 immunotherapy-responsive patients (5 cores), as well as 6 immunotherapy naïve patients (12 cores). Through high-plex, regional-based transcriptomic mapping of the tumor and TME, pathways involved with the complement system and hypoxia were identified to be differentially expressed in patients who went on to experience a poor immunotherapy response. Single-cell, targeted proteomic analysis found that immune cell infiltration of the cancer cell mass and interactions of CD8 T cells with tumor and other immune cells were associated with positive immunotherapy response. The relative abundance of specific tumor phenotypes and their interactions with various immune cells was identified to be different between response groups. This study demonstrates how spatial transcriptomics and proteomics can resolve novel alterations in the TME of HNC that may contribute to therapy sensitivity and resistance.

The digital revolution in veterinary pathology

For the past two centuries, the use of traditional light microscopy to examine tissues to make diagnoses has remained relatively unchanged. While the fundamental concept of tissue slide analysis has stayed the same, our interaction with the microscope is undergoing significant changes. Digital pathology (DP) has gained momentum in veterinary science and is on the verge of becoming a vital tool in diagnostics, research and education. Many diagnostic laboratories have incorporated DP as a critical part of their workflows. Innovations in DP and whole slide image technology have made telediagnosis (the process of transmitting digital clinical data using telecommunication networks for distant diagnosis) more accessible, leading to improved patient care through streamlining of workflows and greater accessibility of second opinions. The integration of machine learning and artificial intelligence and human-in-the-loop protocols for DP workflows will further the development of computer-aided diagnosis and prognostic tools. Despite its present weaknesses, DP will progressively aid veterinary clinicians and pathologists in delivering more accurate and reliable diagnoses. Consistent incorporation of DP frontline advancements into routine veterinary diagnostic pipelines will assist in improving current tools and help prepare pathologists for the progression of digitalization in the field.

DNA methylation controls stemness of astrocytes in health and ischaemia

Astrocytes are the most abundant cell type in the mammalian brain and provide structural and metabolic support to neurons, regulate synapses and become reactive after injury and disease. However, a small subset of astrocytes settles in specialized areas of the adult brain where these astrocytes instead actively generate differentiated neuronal and glial progeny and are therefore referred to as neural stem cells1-3. Common parenchymal astrocytes and quiescent neural stem cells share similar transcriptomes despite their very distinct functions4-6. Thus, how stem cell activity is molecularly encoded remains unknown. Here we examine the transcriptome, chromatin accessibility and methylome of neural stem cells and their progeny, and of astrocytes from the striatum and cortex in the healthy and ischaemic adult mouse brain. We identify distinct methylation profiles associated with either astrocyte or stem cell function. Stem cell function is mediated by methylation of astrocyte genes and demethylation of stem cell genes that are expressed later. Ischaemic injury to the brain induces gain of stemness in striatal astrocytes7. We show that this response involves reprogramming the astrocyte methylome to a stem cell methylome and is absent if the de novo methyltransferase DNMT3A is missing. Overall, we unveil DNA methylation as a promising target for regenerative medicine.

OrgaMapper: a robust and easy-to-use workflow for analyzing organelle positioning

BACKGROUND

Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning.

RESULTS

Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.

CONCLUSIONS

OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning.

Cell-cycle-dependent mRNA localization in P-bodies

Understanding the dynamics of RNA targeting to membraneless organelles is essential to disentangle their functions. Here, we investigate how P-bodies (PBs) evolve during cell-cycle progression in HEK293 cells. PB purification across the cell cycle uncovers widespread changes in their RNA content, partly uncoupled from cell-cycle-dependent changes in RNA expression. Single-molecule fluorescence in situ hybridization (FISH) shows various mRNA localization patterns in PBs peaking in G1, S, or G2, with examples illustrating the timely capture of mRNAs in PBs when their encoded protein becomes dispensable. Rather than directly reflecting absence of translation, cyclic mRNA localization in PBs can be controlled by RBPs, such as HuR in G2, and by RNA features. Indeed, while PB mRNAs are AU rich at all cell-cycle phases, they are specifically longer in G1, possibly related to post-mitotic PB reassembly. Altogether, our study supports a model where PBs are more than a default location for excess untranslated mRNAs.

Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments

The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME imaging data can be mined to elucidate cellular interactions are only beginning to be realized. Here, we present a novel approach for multipronged computational immune synapse analysis (CISA) that reveals T-cell synaptic interactions from multiplex images. CISA enables automated discovery and quantification of immune synapse interactions based on the localization of proteins on cell membranes. We first demonstrate the ability of CISA to detect T-cell:APC (antigen presenting cell) synaptic interactions in two independent human melanoma imaging mass cytometry (IMC) tissue microarray datasets. We then verify CISA's applicability across data modalities with melanoma histocytometry whole slide images, revealing that T-cell:macrophage synapse formation correlates with T-cell proliferation. We next show the generality of CISA by extending it to breast cancer IMC images, finding that CISA quantifications of T-cell:B-cell synapses are predictive of improved patient survival. Our work demonstrates the biological and clinical significance of spatially resolving cell-cell synaptic interactions in the TME and provides a robust method to do so across imaging modalities and cancer types.

Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images

Detecting and segmenting unstained living adherent cells in differential interference contrast (DIC) images is crucial in biomedical research, such as cell microinjection, cell tracking, cell activity characterization, and revealing cell phenotypic transition dynamics. We present a robust approach, starting with dataset transformation. We curated 520 pairs of DIC images, containing 12,198 HepG2 cells, with ground truth annotations. The original dataset was randomly split into training, validation, and test sets. Rotations were applied to images in the training set, creating an interim "α set." Similar transformations formed "β" and "γ sets" for validation and test data. The α set trained a Mask R-CNN, while the β set produced predictions, subsequently filtered and categorized. A residual network (ResNet) classifier determined mask retention. The γ set underwent iterative processing, yielding final segmentation. Our method achieved a weighted average of 0.567 in average precision (AP)0.75bbox and 0.673 in AP0.75segm, both outperforming major algorithms for cell detection and segmentation. Visualization also revealed that our method excels in practicality, accurately capturing nearly every cell, a marked improvement over alternatives.

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

Multimodal imaging of a liver-on-a-chip model using labelled and label-free optical microscopy techniques

A liver-on-a-chip model is an advanced complex in vitro model (CIVM) that incorporates different cell types and extracellular matrix to mimic the microenvironment of the human liver in a laboratory setting. Given the heterogenous and complex nature of liver-on-a-chip models, brightfield and fluorescence-based imaging techniques are widely utilized for assessing the changes occurring in these models with different treatment and environmental conditions. However, the utilization of optical microscopy techniques for structural and functional evaluation of the liver CIVMs have been limited by the reduced light penetration depth and lack of 3D information obtained using these imaging techniques. In this study, the potential of both labelled as well as label-free multimodal optical imaging techniques for visualization and characterization of the cellular and sub-cellular features of a liver-on-a-chip model was investigated. (1) Cellular uptake and distribution of Alexa 488 (A488)-labelled non-targeted and targeted antisense oligonucleotides (ASO and ASO-GalNAc) in the liver-on-a-chip model was determined using multiphoton microscopy. (2) Hyperspectral stimulated Raman scattering (SRS) microscopy of the C-H region was used to determine the heterogeneity of chemical composition of circular and cuboidal hepatocytes in the liver-on-a-chip model in a label-free manner. Additionally, the spatial overlap between the intracellular localization of ASO and lipid droplets was explored using simultaneous hyperspectral SRS and fluorescence microscopy. (3) The capability of light sheet fluorescence microscopy (LSFM) for full-depth 3D visualization of sub-cellular distribution of A488-ASO and cellular phenotypes in the liver-on-a-chip model was demonstrated. In summary, multimodal optical microscopy is a promising platform that can be utilized for visualization and quantification of 3D cellular organization, drug distribution and functional changes occurring in liver-on-a-chip models, and can provide valuable insights into liver biology and drug uptake mechanisms by enabling better characterization of these liver models.

Organellular imaging in vivo reveals a depletion of endoplasmic reticular calcium during post-ictal cortical spreading depolarization

During cortical spreading depolarization (CSD), neurons exhibit a dramatic increase in cytosolic calcium, which may be integral to CSD-mediated seizure termination. This calcium increase greatly exceeds that during seizures, suggesting the calcium source may not be solely extracellular. Thus, we sought to determine if the endoplasmic reticulum (ER), the largest intracellular calcium store, is involved. We developed a two-photon calcium imaging paradigm to simultaneously record the cytosol and ER during seizures in awake mice. Paired with direct current recording, we reveal that CSD can manifest as a slow post-ictal cytosolic calcium wave with a concomitant depletion of ER calcium that is spatiotemporally consistent with a calcium-induced calcium release. Importantly, we observed both naturally occurring and electrically induced CSD suppressed post-ictal epileptiform activity. Collectively, this work links ER dynamics to CSD, which serves as an innate process for seizure suppression and a potential mechanism underlying therapeutic electrical stimulation for epilepsy.

Position-independent functional refinement within the vagus motor topographic map

Motor neurons in the central nervous system often lie in a continuous topographic map, where neurons that innervate different body parts are spatially intermingled. This is the case for the efferent neurons of the vagus nerve, which innervate diverse muscle and organ targets in the head and viscera for brain-body communication. It remains elusive how neighboring motor neurons with different fixed peripheral axon targets develop the separate somatodendritic (input) connectivity they need to generate spatially precise body control. Here, we show that vagus motor neurons in the zebrafish indeed generate spatially appropriate peripheral responses to focal sensory stimulation even when they are transplanted into ectopic positions within the topographic map, indicating that circuit refinement occurs after the establishment of coarse topography. Refinement depends on motor neuron synaptic transmission, suggesting that an experience-dependent periphery-to-brain feedback mechanism establishes specific input connectivity among intermingled motor populations.