Biology subjects

Pham, T.

Publications and source records attributed to Pham, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

VN1K: a genome graph-based and function-driven multi-omics and phenomics resource for the Vietnamese population

Vietnam, the 16th most populated nation, remains profoundly underrepresented in global genomic databases. Here, we present VN1K, a first-ever comprehensive and well-curated resource of multi-omics data with a wide-range of phenotypic information of 1,011 unrelated Vietnamese individuals. High-depth short-read whole-genome sequencing data were generated for all samples along with various - omic data, including microarray, long-read whole-genome sequencing, and RNA sequencing. Using a high-sensitivity variant detection pipeline, which included a pangenome graph reference and a deep-learning framework, we identified nearly 40 million variants of which 8.5 million are novel with nearly 900 thousand short insertions/deletions and 39 thousand structural variants. Specifically, VN1K featured a first-ever whole-genome methylation profile based on long read sequencing. A genotype imputation panel was also created with the highest accuracy on the Vietnamese population. Variants with significantly different allele frequencies in the Vietnamese population compared to others were found to be functionally significant, especially in genes associated with immune diseases (HLA-B, KIR3DL3, KIR2DL1, KIR2DL4) or drug responses (CYP2C19, CYP2D6, VKORC1, CYP2B6). We were also able to map various loci related to hepatitis B virus infection as well as six disease traits, including triglyceride levels, LDL-C, serum glucose levels, HbA1c, and levels of two liver enzymes (ALT and AST). VN1K dataset is accessible via genome.vinbigdata.org, an integrated platform with both linear and graph-based genome browser for facilitating data exploration, research, and applications in precision medicine.

genomics↗

bioRxiv2025

A Multimodal Imaging Pipeline for the Discovery of Molecular Markers of Cellular Neighborhoods

Insights into tissue microenvironments are constrained by profiling cellular and molecular components independently. To address this, we developed an integrated, multimodal workflow combining imaging mass spectrometry and highly multiplexed immunofluorescence on the same sample. Custom computational pipelines enable precise spatial co-registration, facilitating direct integration of cellular and molecular profiles. CODEX-derived cellular and neighborhood masks enable mining of IMS-derived lipid data to identify molecular markers of cell types and spatial neighborhoods in human kidney tissue. We discovered distinct carnitine distributions in cortical and medullary regions, indicating metabolic heterogeneity driven by microenvironment-specific oxygen levels and energy demands. Glomerular cell-type-specific analysis found ether-linked phosphatidylcholine and sphingomyelin, species associated with slit diaphragm integrity, localized to podocytes. Immune cell neighborhoods exhibited molecular signatures consistent with cell signaling and activation pathways in damaged tissue regions. This multi-omic framework links cellular organization with molecular signatures to unveil cellular and molecular relationships underlying tissue function and pathology.

molecular biology↗

bioRxiv2025

CasGen: A Regularized Generative Model for CRISPR Cas Protein Design with Classification and Margin-Based Optimization

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated proteins (Cas) systems have revolutionized genome editing by providing high precision and versatility. However, most genome editing applications rely on a limited number of well-characterized Cas9 and Cas12 variants, constraining the potential for broader genome engineering applications. In this study, we extensively explored Cas9 and Cas12 proteins and developed CasGen, a novel transformer-based deep generative model with margin-based latent space regularization to enhance the quality of newly generative Cas9 and Cas12 proteins. Specifically, CasGen employs a strategies that combine classification to filter out non-Cas sequences, Bayesian optimization of the latent space to guide functionally relevant designs, and thorough structural validation using AlphaFold-based analyses to ensure robust protein generation. We collected a comprehensive dataset with 3,021 Cas9, 597 Cas12, and 597 Non-Cas protein sequences from reputable biological databases such as InterPro and PDB. To validate the generated proteins, we performed sequence alignment using the BLAST tool to ensure novelty and filter out highly similar sequences to existing Cas proteins. Structural prediction using AlphaFold2 and AlphaFold3 confirmed that the generated proteins exhibit high structural similarity to known Cas9 and Cas12 variants, with TM-scores between 0.70 and 0.85 and root-mean-square deviation (RMSD) values below 2.00 [A]. Sequence identity analysis further demonstrated that the generated Cas9 orthologs exhibited 28% to 55% identity with known variants, while Cas12a variants show up to 48% identity. Our results demonstrate that the proposed Cas generative model has significant potential to expand the genome editing toolkit by designing diverse Cas proteins that retain functional integrity. The developed deep generative approach offers a promising avenue for synthetic biology and therapeutic applications, enableling the development of more precise and versatile Cas-based genome editing tools.

genomics↗

bioRxiv2025

Deep learning-driven morphology analysis enables label-free classification of therapeutic agent- naive versus resistant cancer cells

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.

cancer biology↗

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

Sexual Dimorphism of Plasma and Tissue Proteomes in Human Calcific Aortic Valve Stenosis Pathogenesis

BACKGROUNDCalcific aortic valve stenosis (CAVS) is a global clinical burden, impacting around 2% of the population over 65 years of age. No pharmacotherapeutics exist, with surgical repair and transcatheter valve replacement being the only intervention. Females are underrepresented in studies of CAVS, leading to delay in timely intervention and increased mortality. Histopathology demonstrates female CAVS presents with decreased valvular calcification but increased fibrosis and severity of symptoms. We hypothesize that the underlying molecular mechanisms contributing to disease progression and fibrocalcific burden in AS differs between male and female patients. Our goal for this study is to use previously acquired proteomic datasets of a clinically-defined human AS cohort to examine sex disparities and underlying sex-specific disease signatures. METHODS and RESULTSAge-matched human AS tissue samples (n=4 females, n=14 males) were each segmented into non-diseased, fibrotic, and calcified disease stages and analyzed using LC-MS/MS proteomics and quantitative histopathology. CAVD plasma samples (n=20 females, n=30 males) were analyzed for circulating sex-specific biomarkers via LC-MS/MS. Unbiased principal component analysis shows sex- and stage-specific proteome clustering. AS pathogenesis drove sex-specific disparities in the valvular proteome: 338/1503 total proteins were differentially-enriched by sex across disease stages. Compared to sex-specific non-diseased controls, female fibrotic tissue resulted in 2.75-fold greater number of differentially-enriched proteins than did male fibrotic tissue (female: 42, male: 16; p<0.05 threshold). In contrast, female calcific tissue identified 2.473-fold less differentially-enriched proteins than male calcific tissue (female: 157, male 356; q<0.05 threshold). By Functional Enrichment Analysis revealed specific proteins responsible for the exacerbated valvular fibrosis signature in females, implicated adenosine phosphate metabolism as a potential male-specific driver of AS, and further reinforce the shared contribution of aberrant lipid and cholesterol activity to AS progression in both sexes. CONCLUSIONSWe reveal a sexually-dimorphic AS proteome, including the novel overabundance of ECM remodeling pathways in female calcified aortic valve tissues. This analysis allows for identification of potential sex-specific protein drug targets implicated in AS pathobiology.

pathology↗

bioRxiv2024

Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex development

The mammalian cortex is composed of a highly diverse set of cell types and develops through a series of temporally regulated events that build out the cell type and circuit foundation for cortical function. The mechanisms underlying the development of different cell types remain elusive. Single-cell transcriptomics provides the capacity to systematically study cell types across the entire temporal range of cortical development. Here, we present a comprehensive and high-resolution transcriptomic and epigenomic cell type atlas of the developing mouse visual cortex. The atlas was built from a single-cell RNA-sequencing dataset of 568,674 high-quality single-cell transcriptomes and a single-nucleus Multiome dataset of 194,545 high-quality nuclei providing both transcriptomic and chromatin accessibility profiles, densely sampled throughout the embryonic and postnatal developmental stages from E11.5 to P56. We computationally reconstructed a transcriptomic developmental trajectory map of all excitatory, inhibitory, and non-neuronal cell types in the visual cortex, identifying branching points marking the emergence of new cell types at specific developmental ages and defining molecular signatures of cellular diversification. In addition to neurogenesis, gliogenesis and early postmitotic maturation in the embryonic stage which gives rise to all the cell classes and nearly all subclasses, we find that increasingly refined cell types emerge throughout the postnatal differentiation process, including the late emergence of many cell types during the eye-opening stage (P11-P14) and the onset of critical period (P21), suggesting continuous cell type diversification at different stages of cortical development. Throughout development, we find cooperative dynamic changes in gene expression and chromatin accessibility in specific cell types, identifying both chromatin peaks potentially regulating the expression of specific genes and transcription factors potentially regulating specific peaks. Furthermore, a single gene can be regulated by multiple peaks associated with different cell types and/or different developmental stages. Collectively, our study provides the most detailed dynamic molecular map directly associated with individual cell types and specific developmental events that reveals the molecular logic underlying the continuous refinement of cell type identities in the developing visual cortex.

neuroscience↗

bioRxiv2024

Senataxin and DNA-PKcs Redundantly Promote Non-Homologous End Joining Repair of DNA Double Strand Breaks During V(D)J Recombination

Non-homologous end joining (NHEJ) is required for repairing DNA double strand breaks (DSBs) generated by the RAG endonuclease during lymphocyte antigen receptor gene assembly by V(D)J recombination. The Ataxia telangiectasia mutated (ATM) and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) kinases regulate functionally redundant pathways required for NHEJ. Here we report that loss of the senataxin helicase leads to a significant defect in RAG DSB repair upon inactivation of DNA-PKcs. The NHEJ function of senataxin is redundant with the RECQL5 helicase and the HLTF translocase and is epistatic with ATM. Co-inactivation of ATM, RECQL5 and HLTF results in an NHEJ defect similar to that from the combined deficiency of DNA-PKcs and senataxin or losing senataxin, RECQL5 and HLTF. These data suggest that ATM and DNA-PKcs regulate the functions of senataxin and RECQL5/HLTF, respectively to provide redundant support for NHEJ.

cell biology↗

bioRxiv2024

Label-Free Melanoma Phenotype Classification Using Artificial Intelligence-Based Morphological Profiling

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Intratumoral heterogeneity drives varied mutable phenotypes, specifically "melanocytic" and "mesenchymal" cell states, which result in differential functional properties and drug responses. Definitive and rigorous classification of these phenotypic states has been challenging with conventional biomarker-based methods, and high-parameter molecular methods are cell-destructive, labor-intensive, and time-consuming. To overcome these technical and practical limitations, we utilized label-free artificial intelligence-based morphological profiling to classify live melanoma cells into melanocytic and mesenchymal phenotypes based on high resolution imaging of single cells. To predict the phenotypes of single melanoma cells based on morphology alone, we developed the AI-based Melanoma Phenotype Classifier trained with 19 patient-derived cell lines with known melanocytic or mesenchymal transcriptional profiles. To link phenotypic state with high-dimensional morphological profiles, cells were subjected to genetic and chemical perturbations known to shift phenotypic states. The AI classifier successfully predicted phenotypic shifts which were confirmed by single-cell RNA-Seq (scRNA-Seq). These results demonstrate that correlations between melanoma cell phenotypes and morphological changes are detectable by AI. Additionally, the Melanoma Phenotype Classifier was applied to dissociated tumor biopsy samples and characterization of phenotypic heterogeneity was supported by scRNA-Seq transcriptional profiles. This work establishes a link between cell morphology and melanoma phenotypes, laying the groundwork for the use of a label-free morphology-based method for phenotyping live melanoma cells combined with additional analyses.

cancer biology↗

bioRxiv2024

The transcriptomic and spatial organization of telencephalic GABAergic neuronal types

The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.

neuroscience↗

bioRxiv2024

Efficient CRISPR genome editing and integrative genomic analyses reveal the mosaicism of Cas-induced mutations and pleiotropic effects of scarlet gene in an emerging model system

Despite the revolutionary impacts of CRISPR-Cas gene editing systems, the effective and widespread use of CRISPR technologies in emerging model organisms still faces significant challenges. These include the inefficiency in generating heritable mutations at the organismal level, limited knowledge about the genomic consequences of gene editing, and an inadequate understanding of the inheritance patterns of CRISPR-Cas-induced mutations. This study addresses these issues by 1) developing an efficient microinjection delivery method for CRISPR editing in the microcrustacean Daphnia pulex; 2) assessing the editing efficiency of Cas9 and Cas12a nucleases, examining mutation inheritance patterns, and analyzing the local and global mutation spectrum in the scarlet mutants; and 3) investigating the transcriptomes of scarlet mutants to understand the pleiotropic effects of scarlet underlying their swimming behavior changes. Our reengineered CRISPR microinjection method results in efficient biallelic editing with both nucleases. While indels are dominant in Cas-induced mutations, a few on-site large deletions (>1kb) are observed, most likely caused by microhomology-mediated end joining repair. Knock-in of a stop codon cassette to the scarlet locus was successful, despite complex induced mutations surrounding the target site. Moreover, extensive germline mosaicism exists in some mutants, which unexpectedly produce different phenotypes/genotypes in their asexual progenies. Lastly, our transcriptomic analyses unveil significant gene expression changes associated with scarlet knock-out and altered swimming behavior in mutants, including several genes (e.g., NMDA1, ABAT, CNTNAP2) involved in human neurodegenerative diseases. This study expands our understanding of the dynamics of gene editing in the tractable model organism Daphnia and highlights its promising potential as a neurological disease model.

genomics↗

bioRxiv2024

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.

bioinformatics↗

bioRxiv2024

Discovery and engineering of the antibody response against a prominent skin commensal

The ubiquitous skin colonist Staphylococcus epidermidis elicits a CD8+ T cell response pre-emptively, in the absence of an infection1. However, the scope and purpose of this anti-commensal immune program are not well defined, limiting our ability to harness it therapeutically. Here, we show that this colonist also induces a potent, durable, and specific antibody response that is conserved in humans and non-human primates. A series of S. epidermidis cell-wall mutants revealed that the cell surface protein Aap is a predominant target. By colonizing mice with a strain of S. epidermidis in which the parallel {beta}-helix domain of Aap is replaced by tetanus toxin fragment C, we elicit a potent neutralizing antibody response that protects mice against a lethal challenge. A similar strain of S. epidermidis expressing an Aap-SpyCatcher chimera can be conjugated with recombinant immunogens; the resulting labeled commensal elicits high titers of antibody under conditions of physiologic colonization, including a robust IgA response in the nasal mucosa. Thus, immunity to a common skin colonist involves a coordinated T and B cell response, the latter of which can be redirected against pathogens as a novel form of topical vaccination.

microbiology↗

bioRxiv2023

Enhancement of antibody thermostability and affinity by computational design in the absence of antigen

Over the last two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used to design 200 potentially stabilized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb can enhance the stability of these antibody variants without relying on or predicting the antibody-antigen interface, and whether this stabilization could increase antibody affinity without impacting their developability profile. The 200 variants were produced through a robust highthroughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (non-specific binding, aggregation propensity, self-association). In the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5-to 21-fold increase), with most clones retaining the favorable developability profile of the parental antibody. These data open the possibility of in silico antibody stabilization and affinity maturation without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

molecular biology↗

bioRxiv2023

Phosphene and Motor Transcranial Magnetic Stimulation Thresholds Are Correlated: A Meta-Analytic Investigation

Transcranial magnetic stimulation (TMS) is commonly delivered at an intensity defined by the resting motor threshold (rMT), which is thought to represent cortical excitability, even if the TMS target area falls outside of the motor cortex. This approach rests on the assumption that cortical excitability, as measured through the motor cortex, represents a global measure of excitability. Another common approach to measure cortical excitability relies on the phosphene threshold (PT), measured through the visual cortex of the brain. However, it remains unclear whether either estimate can serve as a singular measure of global cortical excitability. If PT and rMT indeed reflect global cortical excitability, they should be correlated. To test this, we systematically identified previous studies that measured PT and rMT to calculate an overall correlation between the two estimates. Our results, based on 16 effect sizes from eight studies, indicated that PT and rMT are correlated ({rho} = .4), and thus one measure could potentially serve as a global cortical excitability measure. Three exploratory meta-analyses revealed that the strength of the correlation is affected by different methodologies, and that PT intensities are higher than rMT. Evidence for a PT-rMT correlation remained robust across all analyses. Further research is necessary for an in-depth understanding of how cortical excitability is reflected through TMS.

neuroscience↗

bioRxiv2023

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

Cancer genome data has been growing in both size and complexity, primarily driven by advances in next-generation sequencing technologies, such as Pan-cancer data from TCGA, ICGC, and single-cell sequencing. Yet, discerning the functional role of individual genomic lesions remains a substantial challenge due to the complexity and scale of the data. Previously, we introduced REVEALER, which identifies groups of genomic alterations that significantly associate with target functional profiles or phenotypes, such as pathway activation, gene dependency, or drug response. In this paper, we present a new mathematical formulation of the algorithm. This version (REVEALER 2.0) is considerably more powerful than the original, allowing for rapid processing and analysis of much larger datasets and facilitating higher-resolution discoveries at the level of individual alleles. REVEALER 2.0 employs the Conditional Information Coefficient (CIC) to pinpoint features that are either complementary or mutually exclusive but still correlate with the target functional profile. The aggregation of these features provides a better explanation for the target functional profile than any single alteration on its own. This is indicative of scenarios where several activating genomic lesions can initiate or stimulate a key pathway or process. We replaced the initial three-dimensional kernel estimation with multiple precomputed one-dimensional kernel estimations, resulting in an approximate 150x increase in speed and efficiency. This improvement, combined with its efficient execution, makes REVEALER 2.0 suitable for much larger datasets and a more extensive range of genomic challenges.

bioinformatics↗

bioRxiv2023

Ultrasensitive and multiplexed protein imaging with clickable and cleavable fluorophores

Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity or technically demanding. To tackle these issues, here we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through reiterative cycles of target staining, fluorescence imaging, and fluoropohore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in a human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, [~]820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed different subregions of the tissue are composed of cells from specific clusters.

bioengineering↗

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↗