Biology subjects

Jin, K.

Publications and source records attributed to Jin, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from [~]15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types--L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

neuroscience↗

bioRxiv2025

Technical and biological sources of noise confound multiplexed enhancer AAV screening

Cis-acting regulatory enhancer elements are valuable tools for gaining cell type-specific genetic access. Leveraging large chromatin accessibility atlases, putative enhancer sequences can be identified and deployed in adeno-associated virus (AAV) delivery platforms. However, a significant bottleneck in enhancer AAV discovery is charting their detailed expression patterns in vivo, a process that currently requires gold-standard one-by-one testing. Here we present a barcoded multiplex strategy for screening enhancer AAVs at cell type resolution using single cell RNA sequencing and taxonomy mapping. We executed a proof-of-concept study using a small pool of validated enhancer AAVs expressing in a variety of neuronal and non-neuronal cell types across the mouse brain. Unexpectedly, we encountered substantial technical and biological noise including chimeric packaging products, necessitating development of novel techniques to accurately deconvolve enhancer expression patterns. These results underscore the need for improved methods to mitigate noise and highlight the complexity of enhancer AAV biology in vivo.

neuroscience↗

bioRxiv2024

Upstream open reading frames buffer translational variability during Drosophila evolution and development

Protein abundance tends to be more evolutionarily conserved than mRNA levels both within and between species, yet the mechanisms underlying this phenomenon remain largely unknown. Upstream open reading frames (uORFs) are widespread cis-regulatory elements in eukaryotic genomes that regulate translation, but it remains unclear whether and how uORFs contribute to stabilizing protein levels. In this study, we performed ribosome translation simulations on mRNA to quantitatively assess the extent to which uORF translation influences the translational variability of downstream coding sequences (CDSs) across varying contexts. Our simulations revealed that uORF translation dampens CDS translational variability, with buffering capacity increasing in proportion to uORF efficiency, length, and number. We then compared the translatomes at different developmental stages of two Drosophila species, demonstrating that uORFs buffer mRNA translation fluctuations during both evolution and development. Experimentally, deleting a uORF in the bicoid (bcd) gene--a prominent example of translational buffering--resulted in extensive changes in gene expression and phenotypes in Drosophila melanogaster. Additionally, we observed uORF-mediated buffering between primates and within human populations. Together, our results reveal a novel regulatory mechanism by which uORFs stabilize gene translation during development and across evolutionary time.

evolutionary biology↗

bioRxiv2024

Interpretable scRNA-seq Analysis with Intelligent Gene Selection

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) data analysis faces multiple challenges, including high dimensionality, significant noise, and data loss. To effectively address these issues, we introduce AIGS, a robust and transparent single-cell analysis framework. AIGS utilizes an intelligent gene selection method that systematically identifies the most informative genes for clustering based on the normalized mutual information between pre-learned pseudo-labels and quantified genes. Additionally, AIGS incorporates a scale-invariant distance metric to assess cell-to-cell similarity, enhancing connections between homogenous cells and ensuring more accurate and robust results. Through comprehensive comparisons with state-of-the-art techniques, AIGS demonstrates superior performance in both clustering accuracy and multi-resolution visualization quality. Our in-depth analysis of clustering and visualization results further reveals that AIGS can uncover complex, stage-specific gene expression patterns during the same developmental cell stage.

bioinformatics↗

bioRxiv2024

Species wide inventory of Arabidopsis thaliana organellar variation reveals ample phenotypic variation for photosynthetic performance

Efforts to improve photosynthetic performance are increasingly employing natural genetic variation. However, genetic variation in the organellar genomes (plasmotypes) is often disregarded due to the difficulty of studying the plasmotypes and the lack of evidence that this is a worthwhile investment. Here, we systematically phenotyped plasmotype diversity using Arabidopsis thaliana as a model species. A reanalysis of whole genome resequencing data of 1,531 representative accessions shows that the genetic diversity amongst the mitochondrial genomes is eight times lower than amongst the chloroplast genomes. Plasmotype diversity of the accessions divides the species into two major phylogenetic clusters, within which highly divergent subclusters are distinguished. We combined plasmotypes from 60 A. thaliana accessions with the nuclear genomes (nucleotypes) of four A. thaliana accessions to create a panel of 232 novel cytonuclear genotypes (cybrids). The cybrid plants were grown in a range of different light and temperature conditions and phenotyped using high-throughput phenotyping platforms. Analysis of the phenotypes showed that several plasmotypes alone or in interaction with the nucleotypes have significant effects on photosynthesis, and that the effects are highly dependent on the environment. Moreover, we introduce Plasmotype Association Studies (PAS) as a novel method to reveal plasmotypic effects. Within A. thaliana, several organellar variants can influence photosynthetic phenotypes, which emphasizes the valuable role this variation has on improving photosynthetic performance. The increasing feasibility of producing cybrids in various species calls for further research into how these phenotypes may support breeding goals in crop species. Significance statementPhotosynthesis is one of the few crop traits that has been largely unaddressed which can contribute to increasing crop yield potential. Exploiting genetic variation within organellar genomes presents a promising, yet untapped resource to improve photosynthesis. However, the extent of organellar variation and its impact on photosynthesis within a species remains largely unknown. Using Arabidopsis thaliana as a model species, we revealed highly divergent clusters of organellar variation. We constructed 232 novel combinations of species representative organellar and nuclear genomes, referred to as cybrids. High-throughput phenotyping of these cybrids revealed that organellar variants can substantially impact photosynthesis in different environments. These findings indicate that organellar genomes may be a valuable resource for improving photosynthesis in crops.

plant biology↗

bioRxiv2024

Sacrificing Adaptability for Functionality: The Ivory Tower of Macular Muller Cells

The predilection of many retinal diseases for the macula suggests it may be less resistant to stress than the peripheral retina. Profiling of single-cell level transcriptional changes found that the peripheral retina exhibited more transcriptional changes than the macula in response to stress. One pronounced change was in a subgroup of Muller cells (MCs) that were dominant in the peripheral retina. Genes more abundantly expressed in peripheral MCs were mainly associated with stress responses and were more influenced by light stress. In contrast, genes highly expressed in MCs that predominated in the macula had roles in cellular function and were less influenced by light stress. Metallothionein 1, A Kinase Anchor Protein 12 and MAF BZIP Transcription Factor F were more abundantly expressed in peripheral MCs than in macular MCs. Knockdown of these genes in primary MCs reduced their viability under stress. Our findings indicate that macular MCs are more directed toward maintaining retinal function rather than mounting a stress response when exposed to stress, which may contribute to the maculas vulnerability to degenerative diseases.

neuroscience↗

bioRxiv2023

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings

Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.

bioinformatics↗

bioRxiv2023

Cell-type specific molecular signatures of aging revealed in a brain-wide transcriptomic cell-type atlas

Biological aging can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function. Aging is a complex and dynamic process which influences distinct cell types in a myriad of ways. The cellular architecture of the mammalian brain is heterogeneous and diverse, making it challenging to identify precise areas and cell types of the brain that are more susceptible to aging than others. Here, we present a high-resolution single-cell RNA sequencing dataset containing [~]1.2 million high-quality single-cell transcriptomic profiles of brain cells from young adult and aged mice across both sexes, including areas spanning the forebrain, midbrain, and hindbrain. We find age-associated gene expression signatures across nearly all 130+ neuronal and non-neuronal cell subclasses we identified. We detect the greatest gene expression changes in non-neuronal cell types, suggesting that different cell types in the brain vary in their susceptibility to aging. We identify specific, age-enriched clusters within specific glial, vascular, and immune cell types from both cortical and subcortical regions of the brain, and specific gene expression changes associated with cell senescence, inflammation, decrease in new myelination, and decreased vasculature integrity. We also identify genes with expression changes across multiple cell subclasses, pointing to certain mechanisms of aging that may occur across wide regions or broad cell types of the brain. Finally, we discover the greatest gene expression changes in cell types localized to the third ventricle of the hypothalamus, including tanycytes, ependymal cells, and Tbx3+ neurons found in the arcuate nucleus that are part of the neuronal circuits regulating food intake and energy homeostasis. These findings suggest that the area surrounding the third ventricle in the hypothalamus may be a hub for aging in the mouse brain. Overall, we reveal a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal aging that will serve as a foundation for the investigation of functional changes in the aging process and the interaction of aging and diseases.

neuroscience↗

bioRxiv2023

AI-Designed, Mutation-Resistant Broad Neutralizing Antibodies Against Multiple SARS-CoV-2 Strains

In this study, we generated a Digital Twin for SARS-CoV-2 by integrating data and meta-data with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled the computational design of broadly neutralizing antibodies against over 1300 different historical strains of SARS-COV-2 containing 64 mutations in the receptor binding domain (RBD) region. The AI-designed antibodies were experimentally validated in real-virus neutralization assays against multiple strains including the newer Omicron strains that were not included in the initial design base. Many of these antibodies demonstrate strong binding capability in ELISA assays against the RBD of multiple strains. These results could help shape future therapeutic design for existing strains, as well as predicting hidden patterns in viral evolution that can be learned by AI for developing future antiviral treatments.

bioinformatics↗

bioRxiv2023

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single- cell RNA-sequencing (scRNA-seq) dataset of [~]7 million cells profiled, and a spatially resolved transcriptomic dataset of [~]4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

neuroscience↗

bioRxiv2022

Profiling of chimeric RNAs in human retinal development with retinal organoids

Chimeric RNAs have been found in both cancer and healthy human cells. They have regulatory effects on human stem/progenitor cell differentiation, stemness maintenance and central nervous system (CNS) development. However, their physiological functions in the retinal development remain unknown. Based on the human embryonic stem cells (hESC)-derived retinal organoids (ROs) spanning from day 0 to day 120, we present the expression atlas of chimeric RNAs throughout the developing ROs. We confirmed the existence of some common chimeric RNAs and also discovered many novel chimeric RNAs during retinal development. We focused on CTNNBIP1-CLSTN1 (CTCL) whose downregulation causes precocious neuronal differentiation and a marked reduction of neural progenitors in human cerebral organoids. Our study found that CTCL also plays a key role in human retinogenesis, CTCL loss-of-function obstructed RO differentiation but prompted the retinal pigment epithelial (RPE) differentiation. Together, this work provides a landscape of chimeric RNAs and reveals evidence for their crucial roles in human retina development.

neuroscience↗

bioRxiv2022

Ancient DNA Reveals China as a Historical Genetic Melting Pot in Tiger Evolution

The contrast between the tigers (Panthera tigris) 2-3 My age and extant tigers coalescence approximately 110,000 years ago suggests an ancient demographic bottleneck. Here we collected over 60 extinct specimens across mainland Asia and generated whole genome sequences from a 10,600-year-old Russian Far East (RFE) specimen (RUSA21, 8{xi} coverage), 14 South China tigers (0.1-12{xi}), three Caspian tigers (4-8{xi}), plus 17 new mitogenomes. RUSA21 clustered within modern Northeast Asian phylogroups and partially derived from an extinct Late Pleistocene lineage. While some 8,000-10,000-year-old RFE mitogenomes are basal to all tigers, one 2,000-year-old specimen resembles present Amur tigers. The Caspian tiger likely dispersed from an ancestral Northeast Asian population and experienced gene flow from southern Bengal tigers. Lastly, genome-wide monophyly supported the South China tiger as a distinct subspecies, albeit with mitochondrial paraphyly, hence resolving its longstanding taxonomic controversy. The distribution of mitochondrial haplogroups corroborated by biogeographical modeling suggested Southwest China was a Late Pleistocene refugium for a relic basal lineage. As suitable habitat returned, Eastern China became a genetic melting pot to foster divergent lineages to merge into South China tigers and other subsequent northern subspecies to develop. Genomic information retrieved from ancient tigers hence sheds light on the species full evolutionary history leading to nine modern subspecies and resolves the natural history of surviving tigers.

evolutionary biology↗

bioRxiv2022

Dexamethasone Enhances CAR T Cell Persistence and Function by Upregulating Interleukin 7 Receptor

Dexamethasone (dex) is a glucocorticoid that is a mainstay for treatment of inflammatory pathologies, including immunotherapy-associated toxicities. Dex suppresses the endogenous immune response and is also believed to suppress the function of chimeric antigen receptor (CAR) T cells. However, recent reports observed higher CAR T cell numbers in patients treated with dex, highlighting the rationale for interrogating the specific effects of dex on CAR T cells. Here, we found that dex did not inhibit CAR T cell expansion or function. A single dose of dex during the manufacturing process upregulated the pro-persistence interleukin 7 receptor (IL7R) on CAR T cells and induced expression of genes involved in activation, migration, and persistence. The ex vivo upregulation of IL7R induced by dex significantly enhanced CAR T cell persistence and anti-tumor efficacy in vivo when combined with exogenous IL-7. Moreover, the combination of dex and IL-7 resulted in increased persistence of CAR T cells and led to complete remission of mice. Overall, our studies in both in vitro and in vivo treatment support a positive role of dex on CAR T cell potency and provide insight into the application of glucocorticoids in cellular anti-cancer therapy.

immunology↗

bioRxiv2022

Single cell transcriptomic profiling of human brain organoids reveals developmental timing- and cell-type-specific vulnerabilities induced by NRXN1 CNVs in schizophrenia

De novo mutations and copy number deletions in NRXN1 (2p16.3) pose a significant risk for schizophrenia (SCZ). It is unclear how NRXN1 deletions impact cortical development in a cell type-specific manner and disease background modulates these phenotypes. Here, we leveraged human pluripotent stem cell-derived forebrain organoid models carrying NRXN1 heterozygous deletions in isogenic and SCZ patient genetic backgrounds and conducted single-cell transcriptomic analysis over the course of brain organoid development from 3 weeks to 3.5 months. Intriguingly, while both deletions similarly impacted molecular pathways associated with ubiquitin-proteasome system, alternative splicing, and synaptic signaling in maturing glutamatergic and GABAergic neurons, SCZ-NRXN1 deletions specifically perturbed developmental trajectories of early neural progenitors and accumulated disease-specific transcriptomic signatures. Using calcium imaging, we found that both deletions led to long-lasting changes in spontaneous and synchronous neuronal networks, implicating synaptic dysfunction. Our study reveals developmental-timing- and cell-type-dependent actions of NRXN1 deletions in unique genetic contexts.

neuroscience↗

bioRxiv2022

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single cell data analysis. Single cell references with comprehensive cell types, reproducible and functional validated cell identities, and common nomenclatures are much needed by the research community to optimize automated cell type annotation and facilitate data integration, sharing, and collaboration. In the present study, we developed a novel computational pipeline to utilize the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples and constructed "LungMAP CellRef" and "LungMAP CellRef Seed" for both normal human and mouse lungs. "CellRef Seed" has an equivalent prediction power and produces consistent cell annotation as does "CellRef" but improves computational efficiency and simplifies its utilization for fast automated cell type annotation and online visualization. This atlas set incorporates 48 human and 40 mouse well-defined lung cell types catalogued from diverse anatomic locations and developmental time points. Using independent datasets, we demonstrated the utility of our CellRefs for automated cell type annotation analysis of both normal and disease lungs. User-friendly web interfaces were developed to support easy access and maximal utilization of the LungMAP CellRefs. LungMAP CellRefs are freely available to the pulmonary research community through fast interactive web interfaces to facilitate hypothesis generation, research discovery, and identification of cell type alterations in disease conditions.

bioinformatics↗

bioRxiv2022

CellDrift: Inferring Perturbation Responses in Temporally-Sampled Single Cell Data

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories, and outcomes.

bioinformatics↗

bioRxiv2022

Social determinants of health and disease in companion dogs: A cohort study from the Dog Aging Project

Exposure to social environmental adversity is associated with health and survival in many social species, including humans. However, little is known about if and how these health and mortality effects vary across the lifespan, largely due to the difficulty of studying long-lived organisms across much of their lifespan. Here, we leveraged a relatively new and powerful model for human aging, the companion dog, to investigate which components of the social environment are associated with dog health and how these associations vary across the lifespan. We drew on comprehensive survey data collected on 21,410 dogs from the Dog Aging Project and identified five factors that together explained 33.7% of the variation in a dogs social environment. Factors capturing financial and household adversity were associated with poorer health and lower physical mobility in companion dogs, while factors that captured social support, such as living with other dogs, were associated with better health when controlling for dog age and weight. Some of these associations differed across a dogs lifespan, including a stronger relationship between owner age and health in younger (as compared to older) dogs. Taken together, these findings suggest the importance of income, stability, and owner age on owner-reported health outcomes in companion dogs and point to potential behavioral and/or environmental modifiers that can be used to promote healthy aging across species.

evolutionary biology↗

bioRxiv2022

KAT5 activity regulates G0-like states in human gliomas

In solid tumors, G0-like states are likely critical for maintaining developmental hierarchies and cellular heterogeneity and promoting tumor growth/recurrence, yet little is known about tumor G0 states or regulation of their ingress/egress. To discover G0-like states and their regulators for glioblastoma (GBM), we analyzed G0 populations in an orthotopic model of GBM using single cell RNA-seq and performed a genome-wide CRISPR-Cas9 screen in patient-derived GBM stem-like cells (GSCs) for genes that trap cells in G0 when inhibited. We identify the protein acetyltransferase KAT5 as a key regulator of transcriptional, epigenetic, and proliferative heterogeneity impacting transitions into G0-like states. KAT5 activity suppresses the emergence of non-dividing subpopulations with oligodendrocyte progenitor and radial glial cell characteristics both in vitro and in a human GSC brain tumor model. In primary gliomas, KAT5 activity is dynamic with KAT5low tumor cells displaying quiescent properties, while KAT5 activity overall increases from low to high grade tumors and is associated with worse patient outcomes.

cancer biology↗