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Results for “Cell Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Identification of antineoplastic agents for oral squamous cell carcinoma: an integrated bioinformatics approach using differential gene expression and network biology

Oral squamous cell carcinoma (OSCC) is the most common malignant epithelial neoplasm and anatomical subtype of head and neck squamous cell carcinoma (HNSCC) with an average 5-year survival rate of less than 50%. To improve the survival rate of OSCC, the discovery of novel anti-cancer drugs is urgently needed. In the present study, we performed metanalysis of 5 gene expression datasets (GSE23558, GSE25099, GSE30784, GSE37991 and TCGA-OSCC) that resulted in 1851 statistically significant DEGs in OSCC. The DEGs were involved in key biological pathways that drive the progression of OSCC. A comprehensive protein-protein interaction (PPI) network was constructed from the DEGs and the top protein clusters (modules) were extracted in Cytoscape. The DEGs from the top modules were searched for antineoplastic agents using L1000CDS2 server. The search resulted in a total of 37 perturbing agents from which 12 well-characterized antineoplastic agents were selected. The selected 12 antineoplastic agents namely Teniposide, Palbociclib, Etoposide, Fedratinib, Tivozanib, Afatinib, Vemurafenib, Mitoxantrone, Idamycin, Canertinib, Dovitinib and Selumetinib. These drugs showed interactions with the over expressed hub genes that regulate cellular proliferation and growth in OSCC progression. These identified antineoplastic agents are candidates for their potential role in treating OSCC.

bioinformatics↗

bioRxiv2017

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesized that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of the deconvolution method used. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We found that immunoStates significantly reduced biological and technical biases. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Importantly, we found that different methods have virtually no effect once the basis matrix is chosen. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

bioinformatics↗

bioRxiv2024

Inferring Single-Cell RNA Kinetics from Various Biological Priors

In the context of transcriptional dynamics modeled by ordinary differential equations (ODEs), the RNA level in a single cell is controlled by specific RNA kinetics parameters, which include transcription rate, splicing rate, and degradation rate. Investigating these single-cell RNA kinetics rates is pivotal for understanding RNA metabolism and the heterogeneity of complex tissues. Although metabolic labeling is an effective method to estimate these kinetics rates experimentally, it is not suitable for current large-scale conventional single-cell RNA sequencing (scRNA-seq) data. Moreover, existing methods for scRNA-seq often either neglect certain specific kinetics parameters or use inappropriate ways to fit the parameters. To address these issues, we introduce scRNAkinetics, a parallelized method that fits the kinetics parameters of the ODE for each cell using pseudo-time derived from various biological priors (e.g. cell lineage tree and differentiation potential). This approach allows for the estimation of the relative kinetics of each cell and gene in a scRNA-seq dataset. Validated on simulated datasets, scRNAkinetics can accurately infer the kinetics rates of transcription boosting, multi-branch, and time-dependent RNA degradation systems. Nevertheless, the inferred kinetics trends are concordant with previous studies on metabolic labeling and conventional scRNA-seq datasets. Furthermore, we show that scRNAkinetics can provide valuable insights into different regulatory schemes and validate the coupling between transcription and splicing in RNA metabolism. The open-source implementation of scRNAkinetics is available at https://github.com/poseidonchan/scRNAkinetics.

bioinformatics↗

bioRxiv2019

Hopper: A Mathematically Optimal Algorithm for Sketching Biological Data

Single-cell RNA-sequencing (scRNA-seq) has grown massively in scale since its inception, presenting substantial analytic and computational challenges. Even simple downstream analyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of gigabytes of memory for todays largest datasets. In addition, current methods often favor common cell types, and miss salient biological features captured by small cell populations. Here we present Hopper, a single-cell toolkit that both speeds up the analysis of single-cell datasets and highlights their transcriptional diversity by intelligent subsampling, or sketching. Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance between the full and downsampled dataset, ensuring that each cell is well-represented by some cell in the sample. Unlike prior sketching methods, Hopper adds points iteratively and allows for additional sampling from regions of interest, enabling fast and targeted multi-resolution analyses. In a dataset of over 1.3 million mouse brain cells, we detect a cluster of just 64 macrophages expressing inflammatory tissues (0.004% of the full dataset) from a Hopper sketch containing just 5,000 cells, and several other small but biologically interesting immune cell populations invisible to analysis of the full data. On an even larger dataset consisting of ~2 million developing mouse organ cells, we show even representation of important cell types in small sketch sizes, in contrast with prior sketching methods. By condensing transcriptional information encoded in large datasets, Hopper grants the individual user with a laptop the same analytic capabilities as large consortium.

bioinformatics↗

bioRxiv2019

Dynamic interactions and intracellular fate of label-free GO within mammalian cells: role of lateral sheet size

Graphene oxide (GO) holds great potential for biomedical applications, however fundamental understanding of the way it interacts with biological systems is still lacking even though it is essential for successful clinical translation. In this study, we exploit intrinsic fluorescent properties of thin GO sheets to establish the relationship between lateral dimensions of the material, its cellular uptake mechanisms and intracellular fate over time. Label-free GO with distinct lateral dimensions, small (s-GO) and ultra-small (us-GO) were thoroughly characterised both in water and in biologically relevant cell culture medium. Interactions of the material with a range of non-phagocytic mammalian cell lines (BEAS-2B, NIH/3T3, HaCaT, 293T) were studied using a combination of complementary analytical techniques (confocal microscopy, flow cytometry and TEM). The uptake mechanism was initially interrogated using a range of pharmaceutical inhibitors and validated using polystyrene beads of different diameters (0.1 and 1 m). Subsequently, RNA-Seq was used to follow the changes in the uptake mechanism used to internalize s-GO flakes over time. Regardless of lateral dimensions, both types of GO were found to interact with the plasma membrane and to be internalized by a panel of cell lines studied. However, s-GO was internalized mainly via macropinocytosis while us-GO was mainly internalized via clathrin- and caveolae-mediated endocytosis. Importantly, we report the shift from macropinocytosis to clathrin-dependent endocytosis in the uptake of s-GO at 24 h, mediated by upregulation of mTORC1/2 pathway. Finally, we show that both s-GO and us-GO terminate in lysosomal compartments for up to 48 h. Our results offer an insight into the mechanism of interaction of GO with non-phagocytic cell lines over time that can be exploited for the design of biomedically-applicable 2D transport systems.

cell biology↗

bioRxiv2025

Identification of Distinct Topological Structures From High-Dimensional Data

Single-cell RNA sequencing allows the direct measurement of the expression of tens of thousands of genes, providing an unprecedented view of the transcriptomic state of a cell. Within each cell, different biological processes such as differentiation or cell cycle take place simultaneously, each contributing a different characterization of cell state. To identify gene sets that govern these processes for the purpose of disentangling convolved biological processes, we present "Identification of Distinct topological structures" (ID). ID works by constructing an alternative low-dimensional parametrization of the high-dimensional system, applying a finite perturbation to this alternative parametrization, and looking for genes that respond similarly. With this approach, we demonstrate that ID is capable of identifying structures within the data that will otherwise be missed. We further demonstrate the utility of ID in scRNA-seq datasets collected under various conditions, delineating cellular differentiation, characterizing cellular response to external perturbation, and dissecting the effect of genetic knock-outs.

bioinformatics↗

bioRxiv2024

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

systems biology↗

bioRxiv2026

Soluble guanylyl cyclase subunits act as Hsp90 co-chaperones to ensure the expression and functional maturation of hemeproteins in mammalian cells

The cofactor Fe-protoporphyrin IX cofactor (heme) performs many functions in biology. Animal cells must stabilize their newly generated heme-free (apo)-hemeproteins and deliver mitochondrial heme to them so they can mature to functional form. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) typically accomplishes the heme deliveries, and for many apo-hemeproteins, heat shock protein 90 (Hsp90) drives their heme insertions. We previously observed hemeproteins express poorly in a cell line (COS-7) that does not express soluble guanylyl cyclase (sGC), a heme-binding enzyme that typically functions through its cGMP generation. To understand sGC involvement, we expressed four hemeproteins, Hemoglobin beta (Hb{beta}), Myoglobin (Mb), Indoleamine 2,3-dioxygenase 1 (IDO1), and Tryptophan 2,3-dioxygenase (TDO) in a cell line expressing sGC (HEK293) or in two cell lines (COS-7, DU145) that do not. We assessed hemeprotein expression levels, their abilities to acquire heme, and when relevant if these facets could be rescued by co-expressing individual sGC subunits, including variants with defects in either sGC heme binding, Hsp90 association, heterodimerization, or cGMP production. We found that co-expression of either sGC subunit was essential for three of the four apo-hemeproteins to accumulate in the COS7 and DU145 cells and acquire heme. This did not involve heme binding, heterodimer formation, or cGMP generation by the sGC subunits, and instead depended on a subunits ability to recruit Hsp90 and GAPDH to the apo-hemeproteins via their own Hsp90 binding. Recruiting Hsp90 and GAPDH to apo-hemeprotein clients to ensure they can accumulate and mature to functional form broadens our understanding of sGC and Hsp90 functions in biology.

cell biology↗

bioRxiv2023

Modelling cell adaptation using internal variables accounting for cell plasticity in continuum mathematical biology

AO_SCPLOWBSTRACTC_SCPLOWCellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favorable/unfavourable conditions or treatments.

cancer biology↗

bioRxiv2024

ATP Regeneration from Pyruvate in the PURE System

The Protein synthesis Using Recombinant Elements ( PURE) system is a minimal biochemical system capable of carrying out cell-free protein synthesis using defined enzymatic components. This study extends PURE by integrating an ATP regeneration system based on pyruvate oxidase, acetate kinase, and catalase. The new pathway generates acetyl phosphate from pyruvate, phosphate, and oxygen, which is used to rephosphorylate ATP in situ. Successful ATP regeneration requires a high initial concentration of[~] 10 mM phosphate buffer, which surprisingly does not affect the protein synthesis activity of PURE. The pathway can function independently or in combination with the existing creatine-based system in PURE; the combined system produces up to 233 {micro}g/ml of mCherry, an enhancement of 78% compared to using the creatine system alone. The results are reproducible across multiple batches of homemade PURE, and importantly also generalise to commercial systems such as PURExpress(R) from New England Biolabs. These results demonstrate a rational bottom-up approach to engineering PURE, paving the way for applications in cell-free synthetic biology and synthetic cell construction.

synthetic biology↗

bioRxiv2022

Cross-activation of the FGF, TGF-β and WNT pathways constrains BMP4-mediated induction of the Totipotent state in mouse embryonic stem cells.

Cell signaling induced cell fate determination is central to stem cell and developmental biology. Embryonic stem cells (ESC) are an attractive model for understanding the relationship between cell signaling and cell fates. Cultured mouse ESCs can exist in multiple cell states resembling distinct stages of early embryogenesis, such as Totipotent, Pluripotent, Primed and Primitive Endoderm. The signaling mechanisms regulating the Totipotent state acquisition and coexistence of these states are poorly understood. Here we identify BMP4 as an inducer of the Totipotent state. However, we discovered that BMP4-mediated induction of the Totipotent state is constrained by the cross-activation of FGF, TGF-{beta} and WNT pathways. We exploited this finding to enhance the proportion of Totipotent cells in ESCs by rationally inhibiting these cross-activated pathways using small molecules. Single-cell mRNA-sequencing further revealed that induction of the Totipotent state is accompanied by the suppression of both the Primed and Primitive Endoderm states. Furthermore, the reprogrammed Totipotent cells generated in culture have a molecular and functional resemblance to Totipotent cell stages of preimplantation embryos. Our findings reveal a novel BMP4 signaling mechanism in ESCs to regulate multiple cell states, potentially significant for managing stem cell heterogeneity in differentiation and reprogramming.

developmental biology↗

bioRxiv2025

A high-throughput heterologous expression platform for plant synthetic biology based on Arabidopsis suspension cells

Efficient heterologous expression platforms are essential for plant synthetic biology, particularly for engineering complex multigene pathways. Here, we establish a high-throughput system for both transient and stable transformation of Arabidopsis thaliana suspension cells using plant cell pack infiltration. This method requires no specialized equipment or consumables and is compatible with several cell lines. It enables rapid generation of 100 g of transgenic cells within two weeks and allows expression of at least 6 stacked genes from a single construct. We characterized constitutive promoters for gene expression in Arabidopsis cells and validated plastid targeting peptides. A library of NifB homologs was screened for expression and solubility and several archaeal variants suitable for plant expression were identified. We further engineered stable cell lines expressing up to six genes, encoding the NifB module components NifU, NifS, FdxN, and NifB, demonstrating that the newly developed platform integrates into an established workflow for nitrogenase engineering. The platform accelerates design-build-test cycles and facilitates the production of delicate proteins that require large amounts of transgenic biomass. It thus represents a versatile and scalable tool for advancing synthetic biology and for tackling major biotechnological challenges, such as biological nitrogen fixation. HighlightWe developed a fast and scalable expression platform in Arabidopsis suspension cells, enabling transient and stable multigene expression for applications in plant synthetic biology such as nitrogenase engineering.

plant biology↗

bioRxiv2024

Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

neuroscience↗

bioRxiv2023

Comparison of three quantitative approaches for estimating time-since-deposition from autofluorescence and morphological profiles of cell populations from forensic biological samples

Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a samples time-since-deposition (TSD), can provide critical context for biological evidence. Yet, there remains no analytical techniques for TSD that are validated for forensic casework. In this study, we investigate whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of touch or trace biological samples. To this end, three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, we transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94-96% and 98-99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework.

molecular biology↗

bioRxiv2019

Development of a Protocol to Study Bronchial Smooth Muscle Cells Behavior on a Natural Biologic Bronchial Matrix

Severe asthma is associated with an increased airway smooth muscle (ASM) mass and an altered composition of the extracellular matrix (ECM). Studies have indicated that ECM-ASM cell interactions contribute to this remodeling and its limited reversibility with current therapy. Three-dimensional matrices allow the study of complex cellular responses to different stimuli in an almost natural environment. Our goal was to obtain acellular bronchial matrices and then develop a recellularization protocol with ASM cells. We studied equine bronchi as horses spontaneously develop a human asthma-like disease. The bronchi were decellularized using Triton/Sodium Deoxycholate. The obtained scaffolds retained their anatomical and histological properties. Using immunohistochemistry and a semi-quantitative score to compare native bronchi to scaffolds revealed no significant variation for matrixial proteins. A DNA quantification and electrophoresis indicated that most of DNA was 29.6 ng/mg of tissue {+/-} 5.6 with remaining fragments of less than 100 bp. Primary ASM cells were seeded on the scaffolds. Histological analysis after recellularization showed that ASM cells migrated and proliferated primarily in the decellularized smooth muscle matrix, suggesting a chemotactic effect of the scaffolds. This is the first report of primary ASM cells preferentially repopulating the smooth muscle matrix layer in bronchial matrices. This protocol is now being used to study the molecular interactions occurring between the asthmatic ECMs and ASM to identify effectors of asthmatic bronchial remodeling.

cell biology↗

bioRxiv2023

Porous membrane electrical cell-substrate impedance spectroscopy for versatile assessment of biological barriers in vitro

Cell culture models of endothelial and epithelial barriers typically use porous membrane inserts (e.g., Transwell inserts) as a permeable substrate on which barrier cells are grown, often in co-culture with other cell types on the opposite side of the membrane. Current methods to characterize barrier function in porous membrane inserts can disrupt the barrier or provide bulk measurements that cannot isolate barrier cell resistance alone. Electrical cell-substrate impedance sensing (ECIS) addresses these limitations but its implementation on porous membrane inserts has been limited by costly manufacturing and low sensitivity. Here we present porous membrane ECIS (PM-ECIS), a cost-effective method to adapt ECIS technology to porous substrate-based in vitro models. We demonstrate high fidelity patterning of electrodes on porous membranes that can be incorporated into well plates of a variety of sizes with excellent cell biocompatibility with mono- and co-culture set ups. PM-ECIS provided sensitive, real-time measurement of isolated changes in endothelial cell barrier impedance with cell growth and barrier disruption. Barrier function characterized by PM-ECIS resistance correlated well with permeability coefficients obtained from molecular tracer permeability assays performed on the same cultures, validating the device. Integration of ECIS into conventional porous cell culture inserts provides a versatile, sensitive, and automated alternative to current methods to measure barrier function in vitro, including molecular tracer assays and transepithelial/endothelial electrical resistance (TEER).

bioengineering↗

bioRxiv2025

Pan-Cancer Single-Cell Profiling Uncovers the Biological Characteristics of Cancer-Testis Genes

Cancer-testis genes (CTGs) are attractive immunotherapeutic targets owing to their restricted testicular expression and aberrant activation in cancers. However, their regulatory mechanisms, spatial organization, and clinical utility remain incompletely understood. Here, we leveraged large-scale single-cell and spatial transcriptomic data to perform a comprehensive pan-cancer analysis of CTGs. We established a high-confidence pan-cancer CTG catalog and uncovered a heterogeneous epigenetic regulatory landscape in which X-linked CTGs are predominantly governed by DNA methylation, whereas autosomal CTGs are more strongly associated with chromatin regulators. Building on the observation that CTG activation is a robust pan-cancer hallmark of malignancy, we developed a computational framework that enables rapid malignant cell annotation with performance comparable to established copy number variation-based methods. Spatial transcriptomic analyses revealed that CTG expression in head and neck squamous cell carcinoma is preferentially enriched at the invasive tumor front. Clinically, we highlighted the underappreciated therapeutic potential of CTGs and prioritized CT83 and DCAF4L2 as promising candidate targets for T-cell receptor-engineered T-cell therapy in triple-negative breast cancer and liver cancer, respectively. Our study advances mechanistic understanding of CTG biology and provides a valuable resource for the development of CTG-based immunotherapies.

cancer biology↗

bioRxiv2020

Cell states beyond transcriptomics: integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

We present a quantitative co-analysis of RNA abundance and sarcomere organization in single cells and an integrated framework to predict subcellular organization states from gene expression. We used human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes expressing mEGFP-tagged alpha-actinin-2 to develop quantitative image analysis tools for systematic and automated classification of subcellular organization. This captured a wide range of sarcomeric organization states within cell populations that were previously difficult to quantify. We performed RNA FISH targeting genes identified by single cell RNA sequencing to simultaneously assess the relationship between transcript abundance and structural states in single cells. Co-analysis of gene expression and sarcomeric patterns in the same cells revealed biologically meaningful correlations that could be used to predict organizational states. This study establishes a framework for multi-dimensional analysis of single cells to study the relationships between gene expression and subcellular organization and to develop a more nuanced description of cell states. Graphical AbstractTranscriptional profiling and structural classification was performed on human induced pluripotent stem cell-derived cardiomyocytes to characterize the relationship between transcript abundance and subcellular organization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/081083v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@8f89c4org.highwire.dtl.DTLVardef@19dc813org.highwire.dtl.DTLVardef@1ba7c20org.highwire.dtl.DTLVardef@2b3daa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗