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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2024

Spatial Modeling Algorithms for Reactions and Transport (SMART) in Biological Cells

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their many functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Herein, we present our software, Spatial Modeling Algorithms for Reaction and Transport (SMART), a package that takes in high-level user specifications about cell signaling networks and molecular transport, and then assembles and solves the associated mathematical and computational systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including YAP/TAZ mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally-derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.

systems biology↗

bioRxiv2024

SubCell: Vision foundation models for microscopycapture single-cell biology

Cell morphology and subcellular protein organization provide important insights into cellular function and behavior. These cellular features can be studied using large-scale fluorescence microscopy, and machine learning has become a powerful tool to interpret the resulting images for biological insights. Here, we introduce SubCell, a deep learning model for fluorescence microscopy designed to accurately capture cellular morphology, protein localization, cellular forganization, and biological function beyond what humans can readily perceive. SubCell was trained on the proteome-wide image collection from the Human Protein Atlas with a novel proteome-aware learning objective. SubCell outperforms state-of-the-art methods across a variety of tasks relevant to single-cell biology and generalizes to other fluorescence microscopy datasets without any fine-tuning. Additionally, we construct the first proteome-wide hierarchical map of proteome organization that is directly learned from image data. This vision-based multiscale cell map defines cellular subsystems down to protein complex resolution, reveals proteins with similar functions, and distinguishes dynamic and stable behaviors within cellular compartments. Finally, combining SubCell with a protein sequence model enables a rich multimodal approach to capture gene function better than either vision-only or sequence-only models alone. In conclusion, SubCell creates deep, image-driven representations of cellular architecture that are applicable across diverse biological contexts and datasets.

cell biology↗

bioRxiv2024

The cell biology and genome of Stentor pyriformis, a giant cell that embeds symbiotic algae in a microtubule meshwork

Endosymbiotic events in which an endosymbiont is retained within a cell that remains capable of phagocytosis, a situation known as mixotrophy, provide potentially important clues about the evolutionary origins of eukaryotes, particularly regarding the relative evolutionary sequence of phagocytosis and endosymbiosis. Mixotrophy in ciliates is commonplace, but has been investigated at a cellular and molecular level almost entirely in one organism, Paramecium bursaria. Reliance on just one model system makes it difficult to know which cell biological aspects of this system represent general features of ciliate mixotrophy versus accidental features of the specific organism. Here we describe the cell biology and genome of the giant heterotrichous ciliate Stentor pyriformis. We show that this giant unicellular organism contains Chlorella variabilis as its endosymbiont, that the Chlorella can live freely outside the host, that within the host the Chlorella cells are docked near the cell surface, surrounded by microtubule "baskets", and that the photosynthetic efficiency of the Chlorella is reduced inside the Stentor cell compared to when it is free-living outside the host, with photon energy instead being shunted to non-photochemical quenching. Compared to the non-mixotrophic Stentor coeruleus, S. pyriformis has several distinct cellular features that may be related to endosymbiosis: the presence of microtubule baskets, which are absent in S. coeruleus; positive rather than negative phototaxis, which is likely an adaptation to allow the photosynthetic symbionts access to sufficient light; and a lack of pigment in the host cell, which may be an adaptation to tolerate high light levels. Compared to P. bursaria, S. pyriformis has several similar cellular features: in both organisms, the symbiont is a strain of Chlorella variabilis; the Chlorella endosymbiont retains the ability to live freely when separated from the host; and the algal symbionts contained in perialgal vesicles are docked at the cell surface. One potentially informative difference between P. bursaria and S. pyriformis is that S. pyriformis employs a standard genetic code, similar to other Stentor species but different from most other ciliates, including P. bursaria, which use a non-standard code in which one or more stop-codons are respecified to encode amino acids. This difference in genetic code could serve as a barrier to impede gene transfer from symbiont to host in other ciliates, but this would not be a factor in S. pyriformis. A second cell biological difference is that whereas P. bursaria performs phototaxis by a kinetic accumulation mechanism, in which swimming is non-directional but cells slow down in regions of higher light intensity, S. pyriformis performs directed swimming towards the direction of high light intensity. However, as in P. bursaria, phototaxis in S. pyriformis requires the presence of the Chlorella, implying a potential flow of information from the symbiont to direct the orientation and swimming of the host cell. We propose that S. pyriformis will serve as a useful model system for studying the evolution of mixotrophy and endosymbiosis, with unique advantages in terms of size and regenerative ability as well as distinct cellular and genomic features compared with other mixotrophic ciliate models.

cell biology↗

bioRxiv2022

The Human Dendritic Cell Atlas: An integrated transcriptional tool to study human dendritic cell biology

Dendritic cells (DCs) are functionally diverse and are present in most adult tissues, however progress in understanding human DC biology is hampered by a relatively small number of these in circulation and by limited access to human tissues. We built a transcriptional atlas of human DCs by combining samples from 14 expression profiling studies derived from 10 laboratories. We identified significant gene expression variation of DC subset-defining markers across tissue-type and upon viral or bacterial stimulation. We further highlight critical gaps between in vitro-derived DC subsets and their in vivo counterparts and provide evidence that monocytes or cord blood progenitor in vitro-differentiated DCs fail to capture the repertoire of primary DC subsets or behaviours. In constructing a reference DC atlas, we provide an important resource for the community wishing to identify and annotate tissue-specific DC subsets from single-cell datasets, or benchmark new in vitro models of DC biology. Key PointsO_LIA reference atlas of human DC that allows benchmarking of in vitro DC models C_LIO_LIMeta-analysis of 14 integrated studies demonstrate that human conventional dendritic cells have distinct tissue-of-origin phenotypes C_LIO_LIUser uploads allow tissue-relevant annotation of human DC subsets from single cell datasets C_LIO_LIKey subset markers are altered by tissue or activation status C_LIO_LIGaps between in vitro-differentiated DC and in vivo counterparts are partially rescued by humanized mouse models, or coculture with NOTCH-ligands. C_LI

immunology↗

bioRxiv2023

Development of an undergraduate cell biology laboratory to assess pigmentation and cell size in a zebrafish model of uveal melanoma

This study outlines a two-week laboratory module for an authentic cell biology undergraduate research experience that uses zebrafish (Danio rerio), a popular model organism for research. Previous research has indicated that course-based undergraduate research experiences such as this one increase student confidence, active learning, and retention. During this research experience, students investigate variations in pigmentation in the caudal fins of wild type and transgenic fish (Tg(mitfa:GNAQQ209L)). The transgenic fish express a hyperactive G protein, GNAQQ209L, under the melanocyte-specific mitfa promoter, offering insights into uveal melanoma, a common eye cancer. Students specifically analyze the black pigmented cells, melanophores, within the caudal fin. We determined that the transgenic zebrafish have increased pigmentation in their caudal fins but smaller melanophores. These results suggest there are more melanophores in the Tg(mitfa:GNAQQ209L) fish compared to the wild type. Future undergraduate research could investigate these cellular differences. This research experience imparts microscopy and image analysis skills and instills the ability to grapple with large datasets, statistical tests, and data interpretation in alignment with biology education principles. Post-lab surveys reveal students attain confidence in the above skills and in handling animals, along with a deeper appreciation for model organism research and its relevance to cancer cell biology.

scientific communication and education↗

bioRxiv2014

Choosing an appropriate modelling framework for analysing multispecies co-culture cell biology experiments

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insufficient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Cell Biology↗

bioRxiv2018

BacStalk: a comprehensive and interactive image analysis software tool for bacterial cell biology

Prokaryotes display a remarkable spatiotemporal organization of processes within individual cells. Investigations of the underlying mechanisms rely extensively on the analysis of microscopy images. Advanced image analysis software has revolutionized the cell-biological studies of established model organisms with largely symmetric rod-like cell shapes. However algorithms suitable for analyzing features of morphologically more complex model species are lacking although such unusually shaped organisms have emerged as treasure-troves of new molecular mechanisms and diversity in prokaryotic cell biology. To address this problem we developed BacStalk a simple interactive and easy-to-use MatLab-based software tool for quantitatively analyzing images of commonly and uncommonly shaped bacteria including stalked (budding) bacteria. BacStalk automatically detects the separate parts of the cells (cell body stalk bud or appendage) as well as their connections thereby allowing in-depth analyses of the organization of morphologically complex bacteria over time. BacStalk features the generation and visualization of concatenated fluorescence profiles along cells stalks appendages and buds to trace the spatiotemporal dynamics of fluorescent markers. Cells are interactively linked to demographs kymographs cell lineage analyses and scatterplots which enables intuitive and fast data exploration and thus significantly speeds up the image analysis process. Furthermore BacStalk introduces a 2D representation of demo- and kymographs enabling data representations in which the two spatial dimensions of the cell are preserved. The software was developed to handle large data sets and to generate publication-grade figures that can be easily edited. BacStalk therefore provides an advanced image analysis platform that extends the spectrum of model organisms for prokaryotic cell biology to bacteria with multiple morphologies and life cycles.\n\nIMPORTANCEProkaryotic cells show a striking degree of subcellular organization. Studies of the underlying mechanisms and their variation among different species greatly enhance our understanding of prokaryotic cell biology. The image analysis software tool BacStalk extracts an unprecedented amount of information from images of stalked bacteria, by generating interactive demographs, kymographs, cell lineages, and scatter plots that aid fast and thorough data analysis and representation. Notably, BacStalk can preserve the two spatial dimensions of cells when generating demographs and kymographs to accurately and intuitively reflect the intracellular organization. BacStalk also performs well on established, non-stalked model organisms with common or uncommon shapes. BacStalk therefore contributes to the advancement of prokaryotic cell biology, as it widens the spectrum of easily accessible model organisms and enables a more intuitive and interactive data analysis and visualization.

microbiology↗

bioRxiv2024

InTraSeq: A Multimodal Assay that Uncovers New Single-Cell Biology and Regulatory Mechanisms

Single-cell RNA sequencing (scRNA-seq) has revolutionized cell biology by enabling the profiling of transcriptomes at a single-cell resolution, leading to important discoveries that have advanced our understanding of cellular and tissue heterogeneity, developmental trajectories, and disease progression. Despite these important advances, scRNA-seq is limited to measuring the transcriptome providing a partial view of cellular function. To address this limitation, multimodal scRNA-seq assays have emerged, allowing for the simultaneous measurement of RNA expression and protein. Intracellular Transcriptomic and Protein Sequencing (InTraSeq), a novel multimodal scRNA-seq technology described here, enables the concurrent measurement of mRNA, surface markers, cytoplasmic proteins, and nuclear proteins within individual cells through oligo-barcoded antibodies. This method offers a comprehensive approach to studying cellular function by combining RNA and protein profiling from the same sample and utilizing a relatively simple protocol. The InTraSeq method enables researchers to expand their view of critical intracellular protein expression including post-translational modifications (PTMs) and transcription factors, allowing for the identification of novel cellular subtypes and states that may be obscured by RNA-based analyses alone. This is particularly valuable in understanding the heterogeneity of cell populations and identifying distinct functional states. In this report, we used InTraSeq to characterize the complex cellular states and regulatory mechanisms during Th17 cell differentiation. We simultaneously profiled RNA and protein expression in over 85,000 cells, capturing transcriptional changes, changes in protein expression and the dynamics of signaling pathways at a high resolution. Our results revealed novel insights into Th17 cell differentiation, including the identification of key regulatory factors and their target genes. By simultaneously measuring mRNA, extra and intra-cellular proteins, signaling proteins, and PTMs, InTraSeq offers a comprehensive understanding of cellular processes and enables the identification of novel regulatory mechanisms.

cell biology↗

bioRxiv2018

A robust method for transfection in choanoflagellates illuminates their cell biology and the ancestry of animal septins

As the closest living relatives of animals, choanoflagellates offer unique insights into animal origins and core mechanisms underlying animal cell biology. However, unlike traditional model organisms, such as yeast, flies and worms, choanoflagellates have been refractory to DNA delivery methods for expressing foreign genes. Here we report the establishment of a robust method for expressing transgenes in the choanoflagellate Salpingoeca rosetta, overcoming barriers that have previously hampered DNA delivery and expression. To demonstrate how this method accelerates the study of S. rosetta cell biology, we engineered a panel of fluorescent protein markers that illuminate key features of choanoflagellate cells. We then investigated the localization of choanoflagellate septins, a family of GTP-binding cytoskeletal proteins that are hypothesized to regulate the multicellular rosette development in S. rosetta. Fluorescently tagged septins localized to the basal pole of S. rosetta single cells and rosettes in a pattern resembling septin localization in animal epithelia. The establishment of transfection in S. rosetta and its application to the study of septins represent critical advances in the growth of S. rosetta as an experimental model for investigating choanoflagellate cell biology, core mechanisms underlying animal cell biology, and the origin of animals.

cell biology↗

bioRxiv2023

Universal Cell Embeddings: A Foundation Model for Cell Biology

Developing a universal representation space for cells which encompasses the tremendous molecular diversity of cell types within the human body and more generally, across species, would be transformative for cell biology. Recent work using single-cell transcriptomic approaches to create molecular definitions of cell types in the form of cell atlases has provided the necessary data for such an endeavor. Here, we present the Universal Cell Embedding (UCE) foundation model. UCE was trained on a corpus of cell atlas data from human and other species in a completely self-supervised way without any data annotations. UCEs modeling approach is to create a unified biological latent space that can represent cells across diverse tissues and species. This universal cell embedding captures important biological variation despite the presence of experimental noise across diverse datasets. An important aspect of UCEs universality is that new cells can be mapped to this embedding space with no additional data labeling, model training or fine-tuning. We applied UCE to create the Integrated Mega-scale Atlas, embedding 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of experiments, dozens of tissues and eight species. We uncovered new insights about the organization of cell types and tissues within this universal cell embedding space, and leveraged it to infer function of newly discovered cell types. UCEs embedding space exhibits emergent behavior, uncovering biology that it was never explicitly trained for, such as identifying developmental lineages and embedding data from novel species not included in the training set. Overall, by enabling a universal representation for every cell state and type, UCE provides a valuable tool for analysis, annotation and hypothesis generation over single cell data.

cell biology↗

bioRxiv2025

Microtubule curling as an efficient readout to uncover fundamental concepts of axonal cell biology

Nerve fibres (aka axons) are the slender, up-to-meter-long processes of nerve cells that wire nervous systems. These delicate structures must survive for an organisms lifetime, making them prime lesion sites in neurodegeneration. Their long-term maintenance requires the homeostasis of complex local cell biology upheld by motor protein-driven transport along microtubule bundles that run uninterrupted along axons. To gain an understanding of axonal homeostasis, we report and discuss here the functional loss of 105 genes from a wide range of cell biological processes using a standardised Drosophila primary neuron system. [~]40% of these gene deficiencies caused microtubule bundle disturbances referred to as microtubule-curling, which we use as an indicator of axonal atrophy. Our live imaging showed that microtubule-curling initiates in areas where axons widen, such as areas close to the soma, branch points or growth cones. In wild-type neurons, any initiated curling was contained, but it persisted as growing footprints in mutant curl-promoting conditions. Closer analyses of 20 curl-promoting conditions suggested a general classification into two groups: mutations affecting axonal physiology cause ROS-mediated microtubule-curling, whereas mutations causing motor hyperactivation or affecting microtubule-regulating proteins cause structurally induced ROS-independent curling. As will be discussed, our data provide consistent support for the previously proposed dependency cycle of local axon homeostasis model which can explain the long-standing conundrum that genes from a wide range of cell biological processes often have mutational links to the same class of inherited neurodegenerative disease.

cell biology↗

bioRxiv2022

Quantitative proteomics and lipidomics of TFG-deficient B cells provide insights into mechanisms of autophagic flux and plasma cell biology

The autophagy-flux-promoting protein TFG (Trk-fused gene) is up-regulated during B cell differentiation into plasma cells and supports survival of CH12 B cells. We hypothesized that quantitative proteomics analysis of CH12tfgKO B cells with intact or blocked autophagy-lysosome flux (via NH4Cl) will identify mechanisms of TFG-dependent autophagy, plasma cell biology and B cell survival. Analysis of CH12WT B cells in the presence of NH4Cl will identify proteins whose presence is continuously regulated by lysosomes independent of TFG. We determined hundreds of proteins to be controlled by TFG and/or NH4Cl. Notably, NH4Cl treatment alone increased the abundance of a cluster of cytosolic and mitochondrial translational proteins while it also reduced a number of proteins. Within the B cell relevant protein pool, BCL10 was reduced, while JCHAIN was increased in CH12tfgKO B cells. Furthermore, TFG regulated the abundance of transcription factors, such as JUNB, metabolic enzymes, such as the short-chain fatty acid activating enzyme ACOT9 or the glycolytic enzyme ALDOC. Gene ontology enrichment analysis revealed that TFG-regulated proteins localized to mitochondria and membrane-bounded organelles. Due to these findings we performed shotgun lipidomics of glycerophospholipids, uncovering that a particular phosphatidylethanolamine (PE) species, 32:0 PE, which lipidates LC3 most efficiently, was less abundant while phosphatidylglycerol (PG) was more abundant in CH12tfgKO B cells. In line with the role of PG as precursor for Cardiolipin (CL), the CL content was higher in CH12tfgKO B cells and addition of PG liposomes to B cells increased the amount of CL. We propose a role for TFG in B cell activation and plasma cell biology via regulation of proteins involved in germinal center and plasma cell development, such as BCL10 or JCHAIN, as well as in lipid homeostasis, mitochondria and metabolism.

immunology↗

bioRxiv2016

Decoupling global biases and local interactions between cell biological variables

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic information of cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu.

Cell Biology↗

bioRxiv2020

Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime

Cell sorting, whereby a heterogeneous cell mixture segregates and forms distinct homogeneous tissues, is one of the main collective cell behaviors at work during development. Although differences in interfacial energies are recognized to be a possible driving source for cell sorting, no clear consensus has emerged on the kinetic law of cell sorting driven by differential adhesion. Using a modified Cellular Potts Model algorithm that allows for efficient simulations while preserving the connectivity of cells, we numerically explore cell-sorting dynamics over unprecedentedly large scales in space and time. For a binary mixture of cells surrounded by a medium, increase of domain size follows a power-law with exponent n = 1/4 independently of the mixture ratio, revealing that the kinetics is dominated by the diffusion and coalescence of rounded domains. We compare these results with recent numerical and experimental studies on cell sorting, and discuss the importance of boundary conditions, space dimension, initial cluster geometry, and finite size effects on the observed scaling. Author summaryCell sorting describes the spontaneous segregation of identical cells in biological tissues. This phenomenon is observed during development or organ regeneration in a variety of biological systems. Minimization of the total surface energy of a tissue, in which adhesion strengh between homotypic and heterotypic cells are different, is one of the mechanisms that explain cell sorting. This mechanism is then similar to the one that drives demixing of two immiscible fluids. Because of the high sensibility of this process to finite-size and finite-time effects, no clear consensus has emerged on the scaling law of cell sorting driven by differential adhesion. Using an efficient numerical code, we were able to investigate this scaling law on very large binary mixtures of cells. We show that on long times, cell sorting obeys a universal power law, which is independent of the mixture ratio.

biophysics↗

bioRxiv2025

Cryogenic Electron Tomography by the Numbers: Charting Underexplored Lineages in Structural Cell Biology

Imaging of cells and their interactions across the whole biosphere, with molecular-scale resolution, is key for understanding structure-function relations. Cryogenic electron tomography (cryo-ET) is a powerful method for obtaining this critical information. However, cryo-ET studies are challenging and often limited to a small number of cell types per study. Here, we bring together cryo-ET data from hundreds of cells and tissues across the biosphere to (i) identify emerging methodological trends, (ii) reduce imaging time and costs, (iii) quantitively compare methods for cell freezing and sectioning and (iv) census cryo-ET species coverage across all domains of life. Comparison of the fraction of cellular material within lamellae across all domains of life reveals an order of magnitude difference between bacteria (9%) and archaea (15%) compared to eukaryotes (1%). We calculate the fraction of cellular material which can be imaged using distinct sectioning methods on multicellular communities and tissues - identifying serial lift-out as a powerful new approach for obtaining more complete cellular depictions. Finally, we show that the biodiversity of current cryo-ET studies is 2-3 order of magnitude lower than in sequence libraries and 4-5 lower than the total predicted on Earth. Our analyses reveal major evolutionary lineages which remain critically understudied and where future cryo-ET research would be most impactful. Significance StatementCryogenic electron tomography (cryo-ET) is a powerful method for imaging inside cells, but it is challenging and typically limited to a few cells per study. We consolidate and quantify data from hundreds of cellular cryo-ET studies across the biosphere. The methodological trends we identify will enhance cryo-ET throughput and heighten biological insight from underexplored lineages of life on Earth.

biophysics↗

bioRxiv2019

Enabling FIB-SEM Systems for Large Volume Connectomics and Cell Biology

Isotropic high-resolution imaging of large volumes provides unprecedented opportunities to advance connectomics and cell biology research. Conventional Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) offers unique benefits such as high resolution (< 10 nm in x, y, and z), robust image alignment, and minimal artifacts for superior tracing of neurites. However, its prevailing deficiencies in imaging speed and duration cap the maximum possible image volume. We have developed technologies to overcome these limitations, thereby expanding the image volume of FIB-SEM by more than four orders of magnitude from 103 {micro}m3 to 3 x 107 {micro}m3 while maintaining an isotropic resolution of 8 x 8 x 8 nm3 voxels. These expanded volumes are now large enough to support connectomic studies, in which the superior z resolution enables automated tracing of fine neurites and reduces the time-consuming human proofreading effort. Moreover, by trading off imaging speed, the system can readily be operated at even higher resolutions achieving voxel sizes of 4 x 4 x 4 nm3, thereby generating ground truth of the smallest organelles for machine learning in connectomics and providing important insights into cell biology. Primarily limited by time, the maximum volume can be greatly extended. Here we provide a detailed description of the enhanced FIB-SEM technology, which has transformed the conventional FIB-SEM from a laboratory tool that is unreliable for more than a few days to a robust imaging platform with long term reliability: capable of years of continuous imaging without defects in the final image stack. An in-depth description of the systematic approach to optimize operating parameters based on resolution requirements and electron dose boundary conditions is also explicitly disclosed. We further explore how this technology unleashes the full potential of FIB-SEM systems, revolutionizing volume electron microscopy (EM) imaging for biology by gaining access to large sample volumes with single-digit nanoscale isotropic resolution.

neuroscience↗

bioRxiv2025

Multiplex analysis of colorectal cancer tissue describes the composition, cell biology and spatial effects of cell-in-cell events and identifies a T cell-dependent prognostic signature

Cell competition is an emerging mechanism in which mammalian tissues maintain homeostasis by eliminating less fit (loser) cells through direct interactions with fitter (winner) neighbouring cells. In cancer, these competitive interactions may drive tumour evolution; however, spatial organisation and clinical relevance of these events remain poorly understood. One mechanism by which winner cells eliminate loser cells is engulfment, resulting in cell-in-cell (CIC) formation. Although CICs have been observed in many tumour types for over a century, their cellular composition, spatial context, interactions with the tumour microenvironment, and biological significance in human cancers remain unclear. Here, we systematically characterised the cellular identity and functional states of CICs in situ, examined their spatial interactions within the tumour microenvironment, and assessed their clinical relevance using spatially resolved single-cell data from a large cohort of colorectal cancer patients. We demonstrate that CICs occur predominantly between cancer cells but also involve cancer stem cell (CSC)-like populations and cytotoxic T cells. Engulfed (inner) cancer and CSC-like cells display molecular features consistent with a loser-cell phenotype, including increased apoptosis and reduced proliferation, whereas outer cancer cells exhibit winner-cell features such as upregulated glycolysis. Live-cell time-lapse experiments demonstrate that glucose accumulates in inner cells during lysosomal degradation following cell engulfment. Spatial analysis further revealed distinct CIC neighbourhoods, which we defined based on proximity to engulfment events. Cells within these regions, particularly CSC-like cells and cytotoxic T cells, exhibit increased metabolic stress, suggesting local competition for nutrients. Importantly, the presence of cytotoxic T cells within CIC neighbourhoods and spatial co-occurrence patterns between cancer cells and CSC-like populations are associated with improved patient outcomes. Together, our findings demonstrate that cell engulfment defines spatially organised competitive niches and may reflect cell competition within complex tumour microenvironments.

cancer biology↗

bioRxiv2026

Evolved differences in microglial cell biology between surface and cave populations of Astyanax mexicanus

Microglia govern multiple aspects of brain architecture and function by eliminating dying cells, stimulating neurogenesis, refining neural connections, and orchestrating immune responses. The Mexican tetra, Astyanax mexicanus, is a powerful model system for investigating the evolution of brain function, yet microglia have not been investigated in this system. A. mexicanus exists as surface-dwelling and cave morphotypes with prominent behavioral and physiological differences. Notably, these evolved behavioral and physiological changes in cavefish, including diminished immune response, sleep, circadian rhythms, and sensory processing, are directly linked to known microglial functions. These observations suggest that evolved differences in microglia may shape brain circuitry adaptations in cavefish. Here we develop an experimental toolbox to examine microglial specification, dynamics, and function in A. mexicanus to perform comparative analysis of microglial cell biology between the surface and cave morphotypes. We find that the cave populations show increased numbers of microglia over developmental time relative to their surface counterparts. Microglia in Astyanax rapidly expand in response to inflammatory cues, distinct from microglial responses in the related teleost, zebrafish. Furthermore, lysosomal compartments of microglia in the cave populations exhibit increased enhanced proteolytic activity and reduced pH relative to surface morphotypes. Together, our observations reveal evolved differences in microglial cell biology between surface and cave populations of A. mexicanus and provide a framework to uncover novel neuroimmune mechanisms underlying the remarkable adaptations of A. mexicanus.

evolutionary biology↗