Biology subjects

Ding, J.

Publications and source records attributed to Ding, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

scSemiProfiler: Advancing Large-scale Single-cell Studiesthrough Semi-profiling with Deep Generative Models andActive Learning

AbstractSingle-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

bioinformatics↗

bioRxiv2023

MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

Fluorescent microscopy imaging is vital to capturing single-cell spatial data, characterizing tissue organization and facilitating comprehensive analysis of cellular state. Advancements in fluorescent microscopy imaging technologies have enabled precise downstream cellular analysis, particularly in cell segmentation. Accurate segmentation of individual cells allows better profiling and understanding of cell properties and behaviors. The majority of existing segmentation methods predominantly concentrate on enhancing segmentation algorithms, and their effectiveness strongly relies on the input stained image quality. Factors such as high cellular density, indistinct cell boundaries, and staining artifacts can result in uneven and low-quality staining, particularly causing missing or unclear membrane staining. These artifacts adversely impact the efficacy of the subsequent cell segmentation methods. To tackle this insufficient membrane staining, we propose a novel approach, MO_SCPLOWEMC_SCPLOW-GAN, to generate high-quality membranes for cells with missing or weak membranes. Inspired by advanced style transfer techniques in computer vision, MO_SCPLOWEMC_SCPLOW-GAN styles the content of the cells with missing or weak membranes into cells with integrated membrane staining. Considering the differences in membrane morphology between epithelial/tumor cells and immune cells, MO_SCPLOWEMC_SCPLOW-GAN deals with tumor and immune cells separately, not only enhancing membrane staining for cells with partially weak membrane signals but also generating membranes for cells with only nuclear channels. The proposed MO_SCPLOWEMC_SCPLOW-GAN is evaluated using the publicly available CosMx dataset. Experimental results demonstrate significant improvements in image staining quality, more accurate representation of membrane morphology characteristics, and better performance in downstream segmentation tasks. MO_SCPLOWEMC_SCPLOW-GAN is flexibly adapted and applied to other spatially resolved transcriptomics datasets, such as MERFISH and FISHseq. Our work provides a new perspective on tackling the challenges in cell segmentation from fluorescent microscopy image restoration. The implementation of MO_SCPLOWEMC_SCPLOW-GAN is open-source and available at the github repository https://github.com/OmicsML/Mem-GAN. The interactive webserver-based demo of MO_SCPLOWEMC_SCPLOW-GAN can be accessed at http://omicsml.ai/memgan.

bioinformatics↗

bioRxiv2023

GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling

Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) has emerged as a powerful technique for investigating open chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remain challenging due to its sparsity and noise. Genome Foundation Models (GFMs), pre-trained on massive DNA sequences, have proven effective at genome analysis. Given that open chromatin regions (OCRs) harbour salient sequence features, we hypothesize that leveraging GFMs sequence embeddings can improve the accuracy and generalizability of scATAC-seq modeling. Here, we introduce the Genome Foundation Embedded Topic Model (GFETM), an interpretable deep learning framework that combines GFMs with the Embedded Topic Model (ETM) for scATAC-seq data analysis. By integrating the DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state specific TF activity both with zero-shot inference and attention mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes.

bioinformatics↗

bioRxiv2023

Vina-GPU 2.1: towards further optimizing docking speed and precision of AutoDock Vina and its derivatives

AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facil-itate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0. The source code and tools for Vina-GPU 2.1 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.1, accompanied by comprehensive instructions and illustrative examples.

developmental biology↗

bioRxiv2023

A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

AO_SCPLOWBSTRACTC_SCPLOWThe fast-growing single-cell analysis community extends the horizon of quantitative analysis to numerous computational tasks. While the tasks hold vastly different targets from each other, existing works typically design specific model frameworks according to the downstream objectives. In this work, we propose a general single-cell analysis framework by unifying common computational tasks as posterior estimation problems. In light of conditional diffusion generative models, we introduce scDiff through the proposed framework and study different conditioning strategies. With data-specific conditions, scDiff achieves competitive performance against state-of-the-art in various benchmarking tasks. In addition, we illustrate the flexibility of scDiff by incorporating prior information through large language models and graph neural networks. Additional few-shot and zero-shot experiments prove the effectiveness of the prior conditioner on scDiff. Our implementation is publicly available at https://github.com/OmicsML/scDiff.

bioinformatics↗

bioRxiv2023

Engineered dsRNA-protein nanoparticles for effective long-distance transport, delivery and gene silencing in plants

Long-distance transport of exogenous biologically active RNA molecules in higher plants has not been reported. Here, we report that cationized bovine serum albumin (cBSA) avidly binds double-stranded beta-glucuronidase RNA (dsGUS RNA) to form nucleic acid-protein nanocomplexes. Using tobacco and poplar plants, we have shown effective uptake and long-distance transport of cBSA/dsGUS RNA nanocomplexes from basal ends of leaf petioles to leaf blades as well as from basal ends of shoots to their apexes and apical leaves. We have further demonstrated that the cBSA/dsGUS RNA nanocomplexes are highly effective in silencing both conditionally inducible DR5-GUS gene and constitutively active 35S-GUS gene in leaf, shoot and shoot meristem tissues. This cBSA/dsRNA delivery technology may provide a convenient, fast, and inexpensive tool for characterizing gene functions in plants, and potentially for in planta gene-editing.

molecular biology↗

bioRxiv2023

CellPLM: Pre-training of Cell Language Model Beyond Single Cells

The current state-of-the-art single-cell pre-trained models are greatly inspired by the success of large language models. They trained transformers by treating genes as tokens and cells as sentences. However, three fundamental differences between single-cell data and natural language data are overlooked: (1) scRNA-seq data are presented as bag-of-genes instead of sequences of RNAs; (2) Cell-cell relations are more intricate and important than inter-sentence relations; and (3) The quantity of single-cell data is considerably inferior to text data, and they are very noisy. In light of these characteristics, we propose a new pre-trained model CellPLM, which takes cells as tokens and tissues as sentences. In addition, we leverage spatially-resolved transcriptomic data in pre-training to facilitate learning cell-cell relationships and introduce a Gaussian mixture prior distribution as an additional inductive bias to overcome data limitation. CellPLM is the first single-cell pre-trained transformer that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x times higher inference speed compared to existing pre-trained models.

bioinformatics↗

bioRxiv2023

Harnessing Agent-Based Modeling in CellAgentChat to Unravel Cell-Cell Interactions from Single-Cell Data

Understanding cell-cell interactions (CCIs) is essential yet challenging due to the inherent intricacy and diversity of cellular dynamics. Existing approaches often analyze global patterns of CCIs using statistical frameworks, missing the nuances of individual cell behavior due to their focus on aggregate data. This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems as collections of autonomous agents governed by biologically inspired principles and rules. Validated against seven diverse single-cell datasets, CellAgentChat demonstrates its effectiveness in detecting intricate signaling events across different cell populations. Moreover, CellAgentChat offers the ability to generate animated visualizations of single-cell interactions and provides flexibility in modifying agent behavior rules, facilitating thorough exploration of both close and distant cellular communications. Furthermore, CellAgentChat leverages ABM features to enable intuitive in silico perturbations via agent rule modifications, pioneering new avenues for innovative intervention strategies. This ABM method empowers an in-depth understanding of cellular signaling interactions across various biological contexts, thereby enhancing in-silico studies for cellular communication-based therapies.

bioinformatics↗

bioRxiv2023

Plasma Glycomic Markers of Accelerated Biological Aging During Chronic HIV Infection

People with HIV (PWH) experience an increased vulnerability to premature aging and inflammation-associated comorbidities, even when HIV replication is suppressed by antiretroviral therapy (ART). However, the factors that contribute to or are associated with this vulnerability remain uncertain. In the general population, alterations in the glycomes of circulating IgGs trigger inflammation and precede the onset of aging-associated diseases. Here, we investigate the IgG glycomes of cross-sectional and longitudinal samples from 1,216 women and men, both living with virally suppressed HIV and those without HIV. Our glycan-based machine learning models indicate that living with chronic HIV significantly accelerates the accumulation of pro-aging-associated glycomic alterations. Consistently, PWH exhibit heightened expression of senescence-associated glycan-degrading enzymes compared to their controls. These glycomic alterations correlate with elevated markers of inflammatory aging and the severity of comorbidities, potentially preceding the development of such comorbidities. Mechanistically, HIV-specific antibodies glycoengineered with these alterations exhibit reduced anti-HIV IgG-mediated innate immune functions. These findings hold significant potential for the development of glycomic-based biomarkers and tools to identify and prevent premature aging and comorbidities in people living with chronic viral infections.

microbiology↗

bioRxiv2023

Divergent patterns of healthy aging across human brain regions at single-cell resolution reveal links to neurodegenerative disease

Given that age is a significant risk factor for multiple neurodegenerative diseases, investigating normal brain aging may help identify molecular events that may contribute to increased disease risk over time. Single-nucleus RNA sequencing (snRNA-seq) enables analysis of gene expression changes within specific cell-types, potentially offering insights into the molecular mechanisms underlying aging. However, most brain snRNA-Seq datasets used age-matched controls from studies focused on pathological processes and have largely been limited to cortical regions. Therefore, there is a need to investigate the non-pathological aging process in brain regions that are vulnerable to age-related diseases. Here, we report a snRNA-seq study of 6 young (20-30 years) and 7 aged (60-85 years) encompassing four different brain regions: the entorhinal cortex, middle temporal gyrus, subventricular zone, and putamen. We captured over 150,000 nuclei that represented 10 broad cell-types. While we did not find statistically significant differences in cell-type proportions with age, region- and cell-type-specific differential expression analyses identified over 8,000 age-associated genes. Notably, within a given cell-type, most of these associations were region-specific. Functional enrichment analyses of the gene sets for each cell-type-region combination revealed diverse biological processes, including multiple hallmarks of aging, such as proteostasis, interactions with cytokines, vesicular trafficking, metabolism, inflammation, and metal ion homeostasis. Overall, our findings suggest that unique cell-types exhibit distinct transcriptional aging profiles both at the cell-type level and across different brain regions.

neuroscience↗

bioRxiv2023

DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes

Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developed DENetwork. This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model, DENetwork enhances the capabilities of traditional differential gene analysis tools, such as DESeq2. Our application of DENetwork to an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms. DENetwork offers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.

bioinformatics↗

bioRxiv2023

Design of multi-epitope vaccine candidate against Brucella type IV secretion system (T4SS)

Objective the dominant epitopes of T and B cells of VirB8 and VirB10 of Brucella type IV. Secretory systems were predicted and analyzed by bioinformatics, and then multi-epitope vaccines were constructed by reverse vaccination. Methods The amino acid sequences of VirB8 and VirB10 were obtained from the UniProt database, T and B cell dominant epitopes were selected and supplemented with adjuvants to construct a multi-epitope vaccine, SOPMA and RoseTTAFold were applied to predict secondary and tertiary structures respectively for immune simulations. Finally, molecular dynamics simulations of iMEV-TLR4 were then performed. Results one cytotoxic T lymphocyte (CTL) epitope, five helper T lymphocyte (HTL) epitopes, two linear B cell epitopes and three conformational B cell epitopes were obtained in VirB8. 1 cytotoxic T lymphocyte (CTL) epitope, 4 helper T lymphocyte (HTL) epitopes, 4 linear B cell epitopes, and 3 conformational B cell epitopes were obtained in VirB10. The resulting multiepitope vaccine is a protein with good antigenicity, hydrophilicity, and stability. Conclusion a novel brucella multiepitope vaccine was designed by reverse vaccination, and this study provides a theoretical basis for further research.

bioinformatics↗

bioRxiv2023

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular resolution. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multi-cellular spots. However, existing datasets for cell type deconvolution are limited in scale, predominantly encompassing data on mice, and are not designed for human immuno-oncology. In order to overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic dataset named SO_SCPLOWPATIALC_SCPLOWCTD, encompassing 1.8 million cells from the human tumor microenvironment across the lung, kidney, and liver. Distinct from existing approaches that primarily depend on single-cell RNA sequencing data as a reference without incorporating spatial information, we introduce Graph Neural Network-based method (i.e., GNNDO_SCPLOWECONVOLVERC_SCPLOW) that effectively utilize the spatial information from reference samples, and extensive experiments show that GNNDO_SCPLOWECONVOLVERC_SCPLOW often outperforms existing state-of-the-art methods by a substantial margin, without requiring single-cell RNA-seq data. To enable comprehensive evaluations on spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from other platforms (e.g., 10x Visium, MERFISH and sci-Space) into pseudo spots, featuring adjustable spot size. The SO_SCPLOWPATIALC_SCPLOWCTD dataset and GNNDO_SCPLOWECONVOLVERC_SCPLOW implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.

bioinformatics↗

bioRxiv2023

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), a ten-year pilot stage of the lifespan CCNP (2013-2032), is an ongoing project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community (https://www.scidb.cn/en/c/ccnp) at the Science Data Bank. Design TypesO_LIAccelerated longitudinal design C_LIO_LIBrain-mind development C_LIO_LIPopulation imaging C_LIO_LIBrain chart C_LIO_LIRepeated measure C_LI MeasurementsO_LIPsychological behaviours C_LIO_LIBiophysical and physical measures C_LIO_LIIntelligence quotient measure C_LIO_LINeuroimaging C_LI Sample Characteristic - OrganismO_LIHomo sapiens C_LI Sample Characteristic - EnvironmentO_LISchool- and community-based sample C_LI Sample Characteristic - LocationO_LIChongqing and Beijing, China C_LI DurationO_LI10 years (2013-2022) C_LI

neuroscience↗

bioRxiv2023

Multiomics of human aortic endothelial cells reveals cell subtypes with heterogeneous responses to canonical endothelial-to-mesenchymal perturbations

ObjectiveEndothelial cells (ECs), macrophages, and vascular smooth muscle cells (VSMCs) are major cell types in atherosclerosis progression, and heterogeneity in EC sub-phenotypes are becoming increasingly appreciated. Still, studies quantifying EC heterogeneity across whole transcriptomes and epigenomes in both in vitro and in vivo models are lacking. Approach and ResultsTo create an in vitro dataset to study human EC heterogeneity, multiomic profiling concurrently measuring transcriptomes and accessible chromatin in the same single cells was performed on six distinct primary cultures of human aortic ECs (HAECs). To model pro-inflammatory and activating environments characteristic of the atherosclerotic microenvironment in vitro, HAECs from at least three donors were exposed to three distinct perturbations with their respective controls: transforming growth factor beta-2 (TGFB2), interleukin-1 beta (IL1B), and siRNA-mediated knock-down of the endothelial transcription factor ERG (siERG). To form a comprehensive in vivo/ex vivo dataset of human atherosclerotic cell types, meta-analysis of single cell transcriptomes across 17 human arterial specimens was performed. Two computational approaches quantitatively evaluated the similarity in molecular profiles between heterogeneous in vitro and in vivo cell profiles. HAEC cultures were reproducibly populated by 4 major clusters with distinct pathway enrichment profiles: EC1-angiogenic, EC2-proliferative, EC3-activated/mesenchymal-like, and EC4-mesenchymal. Exposure to siERG, IL1B or TGFB2 elicited mostly distinct transcriptional and accessible chromatin responses. EC1 and EC2, the most canonically healthy EC populations, were affected predominantly by siERG; the activated cluster EC3 was most responsive to IL1B; and the mesenchymal population EC4 was most affected by TGFB2. Quantitative comparisons between in vitro and in vivo transcriptomes confirmed EC1 and EC2 as most canonically EC-like, and EC4 as most mesenchymal with minimal effects elicited by siERG and IL1B. Lastly, accessible chromatin regions unique to EC2 and EC4 were most enriched for coronary artery disease (CAD)-associated SNPs from GWAS, suggesting these cell phenotypes harbor CAD-modulating mechanisms. ConclusionPrimary EC cultures contain markedly heterogeneous cell subtypes defined by their molecular profiles. Surprisingly, the perturbations used here, which have been reported by others to be involved in the pathogenesis of atherosclerosis as well as induce endothelial-to-mesenchymal transition (EndMT), only modestly shifted cells between subpopulations, suggesting relatively stable molecular phenotypes in culture. Identifying consistently heterogeneous EC subpopulations between in vitro and in vivo models should pave the way for improving in vitro systems while enabling the mechanisms governing heterogeneous cell state decisions.

genomics↗

bioRxiv2023

High-altitude hypoxia exposure inhibits erythrophagocytosis by inducing macrophage ferroptosis in the spleen

High-altitude polycythemia (HAPC) is a chronic condition prevalent in individuals residing at high altitudes (HA), characterized by an augmented production of red blood cells (RBCs) due to hypoxic conditions. Despite its prevalence, the pathophysiological basis and molecular mechanisms driving HAPC remain largely unelucidated. In our study, we utilized a mouse model exposed to hypobaric hypoxia (HH), replicating the environmental conditions experienced at 6000 m above sea level, coupled with in vitro analysis of primary splenic macrophages under 1% O2 to investigate these mechanisms. Our findings revealed that HH exposure significantly stimulates erythropoiesis, leading to erythrocytosis. This was accompanied by a notable sequence of splenic changes, initially characterized by splenic contraction, which subsequently progressed to splenomegaly over 14 days. A key observation was the impairment on the capacity of spleen to process RBCs, primarily attributed to a reduction in splenic macrophages located in the red pulp. Extended HH treatment over 7 and 14 days precipitated increased iron mobilization and the onset of ferroptosis within the spleen. This was evidenced by altered expression levels of iron metabolism and ferroptosis-related proteins, paralleling gene expression patterns observed in human peripheral blood mononuclear cells. Single-cell sequencing of splenic tissue post-HH exposure demonstrated a marked decrease in macrophage populations after 7 days. Our study further disclosed a significant increase in RBCs retention in the spleen following HH exposure, likely a consequence of diminished red pulp macrophages (RPMs) and erythrophagocytosis. This hypothesis was corroborated through comprehensive analyses involving flow cytometry, histological staining, and immunostaining, which collectively provided a detailed understanding of RBCs dynamics under HH conditions. In vitro analyses substantiated the decline in primary splenic macrophages and induction of ferroptosis under hypoxic treatment, effects that were relieved by pre-treatment with the ferroptosis inhibitor ferrostatin-1. Collectively, our data suggest that HH exposure initiates splenic ferroptosis, leading primarily to a decrease in RPMs. This decrease potentially impacts erythrophagocytosis, contributing to increased RBCs retention and subsequent splenomegaly. Such changes could potentially foster continuous RBCs production and accelerate the progress of HAPC. In conclusion, our study highlights the important role of the spleen and splenic macrophages in the pathogenesis of HAPC, providing crucial insights into the disease progression and potential therapeutic targets.

physiology↗

bioRxiv2023

An expanded GCaMP reporter toolkit for functional imaging in C. elegans

In living organisms, changes in calcium flux are integral to many different cellular functions and are especially critical for the activity of neurons and myocytes. Genetically encoded calcium indicators (GECIs) have been popular tools for reporting changes in calcium levels in vivo. In particular, GCaMP, derived from GFP, are the most widely used GECIs and have become an invaluable toolkit for neurophysiological studies. Recently, new variants of GCaMP, which offer a greater variety of temporal dynamics and improved brightness, have been developed. However, these variants are not readily available to the Caenorhabditis elegans research community. This work reports a set of GCaMP6 and jGCaMP7 reporters optimized for C. elegans studies. Our toolkit provides reporters with improved dynamic range, varied kinetics, and targeted subcellular localizations. Besides optimized routine uses, this set of reporters are also well-suited for studies requiring fast imaging speeds and low magnification or low-cost platforms.

neuroscience↗

bioRxiv2023

Modulation of WNT, Activin/Nodal and MAPK Signaling Pathways Increases Arterial Hemogenic Endothelium and Hematopoietic Stem/Progenitor Cell Formation During Human iPSC Differentiation

Several differentiation protocols enable the emergence of hematopoietic stem and progenitor cells (HSPCs) from human induced pluripotent stem cells (iPSCs), yet optimized schemes to promote the development of HSPCs with self-renewal, multilineage differentiation and engraftment potential are lacking. To improve human iPSC differentiation methods, we modulated WNT, Activin/Nodal and MAPK signaling pathways by stage-specific addition of small molecule regulators CHIR99021, SB431542 and LY294002, respectively, and measured the impact on hematoendothelial formation in culture. Manipulation of these pathways provided a synergy sufficient to enhance formation of arterial hemogenic endothelium (HE) relative to control culture conditions. Importantly, this approach significantly increased production of human HSPCs with self-renewal and multilineage differentiation properties, as well as phenotypic and molecular evidence of progressive maturation in culture. Together, these findings provide a stepwise improvement in human iPSC differentiation protocols and offer a framework for manipulating intrinsic cellular cues to enable de novo generation of human HSPCs with functionality in vivo. Significance StatementThe ability to produce functional HSPCs by differentiation of human iPSCs ex vivo holds enormous potential for cellular therapy of human blood disorders. However, obstacles still thwart translation of this approach to the clinic. In keeping with the prevailing arterial-specification model, we demonstrate that concurrent modulation of WNT, Activin/Nodal and MAPK signaling pathways by stage-specific addition of small molecules during human iPSC differentiation provides a synergy sufficient to promote arterialization of HE and production of HSPCs with features of definitive hematopoiesis. This simple differentiation scheme provides a unique tool for disease modeling, in vitro drug screening and eventual cell therapies.

cell biology↗