Biology subjects

Camara, P. G.

Publications and source records attributed to Camara, P. G..

13 recordsLinked to original sources

bioRxiv2026

Morphology-robust quantification of subcellular organization in complex cells

Quantitative analysis of subcellular protein organization is often confounded by variation in cell morphology, limiting the identification and interpretation of localization patterns in fluorescence microscopy data from morphologically complex cells, such as neurons and glia. We introduce CellAligner, an unsupervised framework that uses fused unbalanced Gromov-Wasserstein couplings to map protein distributions from morphologically distinct cells into shared anchor-cell geometries, enabling morphology-robust comparison of subcellular localization. In neuronal imaging benchmarks, applying current image-analysis methods (CellProfiler, Cytoself, Paired Cell Inpainting) to CellAligners anchor-cell representations substantially reduced morphology-associated confounding while approximately doubling their multiclass MCC for localization classification. We demonstrate its biological utility by identifying U18666A-induced lysosomal trafficking defects in human iPSC-derived neurons. To scale the approach, we developed dCellAligner-OT, a fast deep metric learning model that approximates CellAligners optimal transport distances and anchor-cell representations, enabling atlas-scale analyses. CellAligner provides a general framework for morphology-robust analysis of subcellular organization in complex cellular systems.

bioinformatics↗

bioRxiv2026

ImmunoPheno: A Computational Framework for Data-Driven Design and Analysis of Immunophenotyping Experiments

Immunophenotyping is fundamental to characterizing tissue cellular composition, pathogenic processes, and immune infiltration, yet its accuracy and reproducibility remain constrained by heuristic antibody panel design and manual gating. Here, we present ImmunoPheno, an open-source computational platform that repurposes large-scale single-cell proteo-transcriptomic data to guide immunophenotyping experimental design and analysis. ImmunoPheno integrates existing datasets to automate the design of optimal antibody panels, gating strategies, and cell identity annotation. We used ImmunoPheno to construct a harmonized reference (HICAR) comprising 390 monoclonal antibodies and 93 human immune cell populations. Leveraging this resource, we algorithmically designed minimal panels to isolate rare populations, such as MAIT cells and pDCs, which we validated experimentally. We further demonstrate accurate cell identity annotation across publicly available and newly generated cytometry datasets spanning diverse technologies, including spatial platforms like CODEX. ImmunoPheno complements expert curation and supports continual expansion, providing a scalable framework to enhance the accuracy, reproducibility, and resolution of immunophenotyping.

bioinformatics↗

bioRxiv2025

Exploring the Structural Lexicon of the Proteome via Metric Geometry

The three-dimensional structure of proteins is intimately linked to their function, yet establishing comprehensive frameworks for systematically comparing and organizing protein structures across the proteome remains a significant challenge. Here, we introduce GWProt, a computational framework that leverages recent advances in metric geometry, such as Gromov-Wasserstein couplings, for protein structure alignment and analysis. GWProt enables the integration of biochemical information into structural comparisons and introduces the concept of local geometric distortion, a measure that captures local conformational differences. We demonstrate the utility of this framework by identifying conformational switches within individual proteins, detecting functional domains shared among evolutionarily distant viral proteins, revealing topological rearrangements in homologous folds, and uncovering recurrent short structural motifs underlying functional domains across the human proteome. Collectively, these results establish the use of metric geometry as a versatile and quantitative framework for the systematic comparative analysis of protein structures, complementing existing approaches for elucidating protein organization.

bioinformatics↗

bioRxiv2025

Multiple autism genes influence GABA neuron remodeling via distinct developmental trajectories

Variation in over 100 genes are now associated with increased risk for autism and related neurodevelopmental condition, but how this variation results in distinct and overlapping behavioral changes is still not well understood. Recent efforts have focused on screening many autism genes at once for functional and phenotypic convergence, and identified subsets that are crucial for many early steps of neurodevelopment. Few studies have screened later steps of neurodevelopment, circuit function, circuit plasticity, or behaviors. We screened twenty conserved autism-associated genes for impact on experience-dependent neuron remodeling in C. elegans. Loss of unc-44/ANK2, set-4/KMT5B, daf-18/PTEN, gap-2/SYNGAP1, and chd-1/CHD8 increased, while CACNA2D3/unc-36 decreased, neurite outgrowth of the GABAergic DVB neuron in adults. Although daf-18/PTEN, set-4/KMD5B, and unc-44/ANK2 had convergent phenotypes, they arise from distinct temporal trajectories with differential impact on DVB pre-synaptic morphology. Screening for the DVB regulated spicule protraction behavior identified multiple autism genes involved, but only unc-44/ANK2 and CACNA2D3/unc-36 were shared between screens. Application of a metric geometry computational framework (CAJAL) to the DVB morphology dataset identified 5 additional genes that impact DVB morphology, including unc-2/CACNA1A and unc-10/RIMS1, which also significantly impacted behavior. This work defines new regulators and molecular mechanisms of experience-dependent neuron remodeling and circuit plasticity, and further links these processes with conserved autism genes. It also demonstrates the utility of using intact, behavior generating circuits in C. elegans, to screen for novel roles for conserved autism genes.

neuroscience↗

bioRxiv2023

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities such as bulk ATAC-seq data.

bioinformatics↗

bioRxiv2022

A general framework for the combined morphometric, transcriptomic, and physiological analysis of cells using metric geometry

High-resolution imaging-based single-cell profiling has transformed the study of cells in their spatial context. However, the lack of quantitative methods that can summarize the great diversity of complex cell shapes found in tissues and infer associations with other single-cell data modalities limits current analyses. Here, we report a general computational framework for the multi-modal analysis and integration of single-cell morphological data. We build upon metric geometry to construct cell morphology latent spaces, where distances in these spaces indicate the amount of physical deformation needed to change the morphology of one cell into that of another. Using these spaces, we integrate morphological data across technologies and leverage associated single-cell RNA-seq data to infer relations between morphological and transcriptomic cellular processes. We apply this framework to imaging and multi-modal data of neurons and glia to uncover genes related to neuronal plasticity. Our approach represents a strategy for incorporating cell morphological data into single-cell omics analyses.

bioinformatics↗

bioRxiv2022

Combined single-cell RNA-seq profiling and enhancer editing reveals critical spatiotemporal controls over thalamic nuclei formation in the murine embryo

The thalamus is the principal information hub of the vertebrate brain, with essential roles in sensory and motor information processing, attention, and memory. The molecular mechanisms regulating the formation of thalamic nuclei are unclear. We apply longitudinal single-cell RNA-sequencing, regional abrogation of Sonic-hedgehog (Shh), and spatial profiling of gene expression to map the developmental trajectories of thalamic progenitors, intermediate progenitors, and post-mitotic neurons as they coalesce into distinct thalamic nuclei. These data reveal that the complex architecture of the thalamus is established early during embryonic brain development through the coordinated action of four cell differentiation lineages derived from Shh-dependent and independent progenitors. We systematically characterize the gene expression programs that define these lineages across time and demonstrate how their disruption upon Shh depletion causes pronounced locomotor impairment resembling infantile Parkinsons disease. These results reveal key principles of thalamic development and provide mechanistic insights into neurodevelopmental disorders resulting from thalamic dysfunction.

neuroscience↗

bioRxiv2021

Single-cell antigen-specific activation landscape of CAR T infusion product identifies determinants of CD19 positive relapse in patients with ALL

Chimeric antigen receptor-modified (CAR) T cells targeting CD19 have mediated dramatic responses in relapsed/refractory acute lymphoblastic leukemia (ALL), yet a notable number of patients have CD19-positive relapse within one year of treatment. It remains unclear if the long-term response is associated with the characteristics of CAR T cells in infusion products, hindering the identification of biomarkers to predict therapeutic outcomes prior to treatment. Herein we present 101,326 single cell transcriptomes and surface protein landscape from the CAR T infusion products of 12 pediatric ALL patients upon CAR antigen-specific stimulation in comparison with TCR-mediated activation and controls. We observed substantial heterogeneity in the antigen-specific activation states, among which a deficiency of Th2 function was associated with CD19-positive relapsed patients (median remission 9.6 months) compared with very durable responders (remission>54 months). Proteomic profiles also revealed that the frequency of early memory T cell subsets, rather than activation or co-inhibitory signatures could distinguish CD19-positive relapse. Additionally, a deficit of type 1 helper and cytotoxic effector function and an enrichment for terminally differentiated CD8+ T cells exhibiting low cytokine polyfunctionality was associated with initial non-responders. By contrast, the single-cell transcriptomic data of unstimulated or TCR-activated CAR T cells failed to predict clinical responses. In aggregate, our results dissect the landscape of CAR-specific activation states in infusion products that can identify patients who do not develop a durable response to the therapy, and unveil the molecular mechanisms that may inform strategies to boost specific T cell function to maintain long term remission.

immunology↗

bioRxiv2020

Cell Ecosystem and Signaling Pathways of Primary and Metastatic Pediatric Posterior Fossa Ependymoma

Pediatric ependymoma is a devastating brain cancer marked by its relapsing pattern and lack of effective chemotherapies. This shortage of treatments is partially due to limited knowledge about ependymoma tumorigenic mechanisms. Although there is evidence that ependymoma originates in radial glia, the specific pathways underlying the progression and metastasis of these tumors are unknown. By means of single-cell transcriptomics, immunofluorescence, and in situ hybridization, we show that the expression profile of tumor cells from pediatric ependymomas in the posterior fossa is consistent with an origin in LGR5+ stem cells. Tumor stem cells recapitulate the developmental lineages of radial glia in neurogenic niches, promote an inflammatory microenvironment in cooperation with microglia, and upon metastatic progression initiate a mesenchymal program driven by reactive gliosis and hypoxia-related genes. Our results uncover the cell ecosystem of pediatric posterior fossa ependymoma and identify WNT/{beta}-catenin and TGF-{beta} signaling as major drivers of tumorigenesis for this cancer.

cancer biology↗

bioRxiv2020

Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis

Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

cancer biology↗

bioRxiv2019

Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq

Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

genomics↗

bioRxiv2019

Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping

Highly-multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA-seq data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly-multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling annotation of subtle cell populations, spatial patterns of transcription, and interactions between cell types. More generally, it enables the systematic annotation of cell populations in cytometry data. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published highly-multiplexed cytometry and mIHC data.

bioinformatics↗

bioRxiv2018

Single cell transcriptomic analysis of the adult mouse pituitary reveals a novel multi-hormone cell cluster and physiologic demand-induced lineage plasticity

The anterior pituitary gland drives a set of highly conserved physiologic processes in mammalian species. These hormonally-controlled processes are central to somatic growth, pubertal transformation, fertility, lactation, and metabolism. Current models, largely built upon candidate gene based immuno-histochemical and mRNA analyses, suggest that each of the seven hormones synthesized by the pituitary is produced by a specific and exclusive cell lineage. However, emerging evidence suggests more complex models of hormone specificity and cell plasticity. Here we have applied massively parallel single-cell RNA sequencing (scRNA-seq), in conjunction with a set of orthogonal mRNA and protein imaging studies, to systematically map the cellular composition of adult male and female mouse pituitaries at single-cell resolution and in the setting of major physiologic demands. These analyses reveal sex-specific cellular diversity associated with normal pituitary homeostasis, and identify an array of cells with complex complements of hormone-enrichment as well as a series of non-hormone producing interstitial and supporting cell lineages. These scRNA-seq studies identify a major cell population that is characterized by a unique multi-hormone gene expression profile. The detection of dynamic shifts in cellular representations and transcriptome profiles in response to two well-defined physiologic stresses suggests corresponding roles of a number of these clusters in cellular plasticity within the adult pituitary. These studies point to an unanticipated complexity and plasticity in pituitary cellular composition that expands upon current models and concepts of pituitary gene expression and hormone production.

genomics↗