Biology subjects

Kambhampati, S.

Publications and source records attributed to Kambhampati, S..

12 recordsLinked to original sources

bioRxiv2026

Vermeer: Autoregressive generative modeling of microscopy predicts protein localization

Fluorescent microscopy provides a rich view into how proteins localize within cells, but it remains experimentally infeasible to image human proteins across all of the different factors that can impact localization. We introduce Vermeer, a channel-adaptive autoregressive generative model for in silico generation of microscopy images of protein localization. Vermeer conditions generations on protein sequences and landmark stains showing the morphology of cells, which enables it to generalize to unseen proteins and cell lines. We show that Vermeer, trained on the Human Protein Atlas, can generate images with substantially improved perceptual quality and biological fidelity over previous proposals. Additionally, Vermeers autoregressive framework enables flexible generation using varying channel subsets and orderings, enabling zero-shot transfer to data collected under different imaging conditions and channel configurations than those used for training. These results position Vermeer to enable scalable modeling of protein localization and is a step towards generative foundation models that can operate over distinct microscopy datasets. Code is publicly accessible at https://github.com/microsoft/vermeer.git.

cell biology↗

bioRxiv2024

TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learning

Spatial transcriptomics allows for the measurement of gene expression within native tissue context, thereby improving our understanding of how cell states are modulated by their microenvironment. Despite technological advancements, computational methods to link cell states with their microenvironment and perform comparative analysis across different samples and conditions are still underdeveloped. To address this, we introduce TissueMosaic (Tissue MOtif-based SpAtial Inference across Conditions), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets (https://github.com/broadinstitute/TissueMosaic). TissueMosaic effectively maps structurally similar tissue motifs close together in a learned latent space. TissueMosaic further links these motifs to gene expression, enabling the study of how changes in tissue structure impact function. TissueMosaic increases the signal-to-noise ratio of differential expression analysis through a motif enrichment strategy, resulting in more reliable detection of genes that covary with tissue structure. Here, we demonstrate TissueMosaic on high resolution spatial transcriptomics datasets across tissues, learning representations that outperform neighborhood cell-type composition baselines and existing methods on downstream tasks. We highlight genes and pathways in these tissues that are associated with changes in tissue structure across external conditions. These findings underscore the potential of self-supervised learning to significantly advance spatial transcriptomics research.

bioinformatics↗

bioRxiv2024

Modeling Compound Lipid Homeostasis Using Stable Isotope Tracing

Lipids represent the most diverse pool of metabolites found in cells, facilitating compartmentation, signaling, and other functions. Dysregulation of lipid metabolism is linked to disease states such as cancer and neurodegeneration. However, limited tools are available for quantifying metabolic fluxes across the lipidome. To directly measure reaction fluxes encompassing compound lipid homeostasis, we applied stable isotope tracing, liquid chromatography-high-resolution mass spectrometry, and network-based isotopologue modeling to non-small cell lung cancer (NSCLC) models. Compound lipid metabolic flux analysis (CL-MFA) enables the concurrent quantitation of fatty acid synthesis, elongation, headgroup assembly, and salvage reactions within virtually any biological system. Here, we resolve liver kinase B1 (LKB1)-mediated regulation of sphingolipid recycling in NSCLC cells and precision-cut lung slice cultures. We also demonstrate that widely used tissue culture conditions drive cells to upregulate fatty acid synthase flux to supraphysiological levels. Finally, we identify previously uncharacterized isozyme specificity of ceramide synthase inhibitors. These results highlight the ability of CL-MFA to quantify lipid cycling in biological systems to discover biological function and elucidate molecular mechanisms in membrane lipid metabolism.

cell biology↗

bioRxiv2023

Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

genomics↗

bioRxiv2021

A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the isopair: isoLock, autoCredential and anovAlign

Untargeted metabolomics enables direct quantification of metabolites without apriori knowledge of their identity. Liquid chromatography mass spectrometry (LC-MS), a popular method to implement untargeted metabolomics, identifies metabolites via combined mass/charge (m/z) and retention time as mass features. Improvements in the sensitivity of mass spectrometers has increased the complexity of data produced, leading to computational obstacles. One outstanding challenge is calling metabolite mass feature peaks rapidly and accurately in large LC-MS datasets (dozens to thousands of samples) in the presence of measurement and other noise. While existing algorithms are useful, they have limitations that become pronounced at scale and lead to false positive metabolite predictions as well as signal dropouts. To overcome some of these shortcomings, biochemists have developed hybrid computational and carbon labeling techniques, such as credentialing. Credentialing can validate metabolite signals, but is laborious and its applicability is limited. We have developed a suite of three computational tools to overcome the challenges of unreliable algorithms and inefficient validation protocols: isolock, autoCredential and anovAlign. Isolock uses isopairs, or metabolite-istopologue pairs, to calculate and correct for mass drift noise across LC-MS runs. autoCredential leverages statistical features of LC-MS data to amplify naturally present 13C isotopologues and validate metabolites through isopairs. This obviates the need to artificially introduce carbon labeling. anovAlign, an anova-derived algorithm, is used to align retention time windows across samples to accurately delineate retention time windows for mass features. Using a large published clinical dataset as well as a plant dataset with biological replicates across time, genotype and treatment, we demonstrate that this suite of tools is more sensitive and reproducible than both an open source metabolomics pipelines, XCMS, and the commercial software progenesis QI. This software suite opens a new era for enhanced accuracy and increased throughput for untargeted metabolomics.

bioinformatics↗

bioRxiv2021

Cross-organ transcriptomic comparison reveals universal factors during maturation

Various cell types can be derived from stem cells. However, these cells are immature and do not match their adult counterparts in functional capabilities, limiting their use in disease modeling and cell therapies. Thus, it is crucial to understand the mechanisms of maturation in vivo. However, it is unknown if there are genes and pathways conserved across organs during maturation. To address this, we performed a time-series analysis of the transcriptome of the mouse heart, brain, liver, and kidney and analyzed their trajectories over time. In addition, gene regulatory networks were reconstructed to determine overlapping expression patterns. Based on these, we identified commonly upregulated and downregulated pathways across all four organs. Key upstream regulators were also predicted based on the temporal expression of downstream genes. These findings suggest the presence of universal regulators during organ maturation, which may help us develop a general strategy to mature stem cell-derived cells in vitro.

bioinformatics↗

bioRxiv2021

Developmental Effects on Relative Use of PEPCK and NADP-ME Pathways of C4 Photosynthesis in Maize

C4 photosynthesis is an adaptive photosynthetic pathway which concentrates CO2 around Rubisco in specialized bundle sheath cells to reduce photorespiration. Historically, the pathway has been characterized into three different subtypes based on the decarboxylase involved, although recent work has provided evidence that some plants can use multiple decarboxylases, with maize in particular using both the NADP-malic enzyme (NADP-ME) pathway and phosphoenolpyruvate carboxykinase (PEPCK) pathway. Parallel C4 pathways could be advantageous in balancing energy and reducing equivalents between bundle sheath and mesophyll cells, in decreasing the size of the metabolite gradients between cells and may better accommodate changing environmental conditions or source to sink demands on growth. The enzyme activity of C4 decarboxylases can fluctuate with different stages of leaf development, but it remains unclear if the pathway flexibility is an innate aspect of leaf development or an adaptation to the leaf microenvironment that is regulated by the plant. In this study, variation in the two C4 pathways in maize were characterized at nine plant ages throughout the life cycle. Two positions in the canopy were examined for variation in physiology, gene expression, metabolite concentration, and enzyme activity, with particular interest in asparagine as a potential regulator of C4 decarboxylase activity. Variation in C4 and C3 metabolism was observed for both leaf age and canopy position, reflecting the ability of C4 pathways to adapt to changing microenvironments. One Sentence SummaryThe proportion of the two C4 pathways in maize plants is dependent on canopy position and not the age of the leaf.

plant biology↗

bioRxiv2021

Estimating DNA methylation potential energy landscapes from nanopore sequencing data

High-throughput third-generation sequencing devices, such as the Oxford Nanopore Technologies (ONT) MinION sequencer, can generate long reads that span thousands of bases. This new technology opens the possibility of considering a wide range of epigenetic modifications and provides the capability of interrogating previously inaccessible regions of the genome, such as highly repetitive regions, as well as of performing comprehensive allele-specific methylation analysis, among other applications. It is well-known, however, that detection of DNA methylation from nanopore data results in a substantially reduced per-read accuracy when comparing to WGBS, due to noise introduced by the sequencer and its underlying chemistry. It is therefore imperative that methods are developed for the reliable modeling and analysis of the DNA methylation landscape using nanopore data. Here we introduce such method that takes into account the presence of noise introduced by the ONT sequencer and, by using simulations, we provide evidence of its potential. The proposed approach establishes a solid foundation for the development of a comprehensive framework for the statistical analysis of DNA methylation, and possibly of other epigenetic marks, using third-generation sequencing.

bioinformatics↗

bioRxiv2020

Temporal changes in metabolism late in seed development affect biomass composition in soybean

The inverse correlation between protein and oil production in soybeans is well-documented; however, it has been based primarily on the composition of mature seeds. Though this is the cumulative result of events over the course of soybean seed development, it does not convey information specific to metabolic fluctuations during developmental growth regimes. Maternal nutrient supply via seed coat exudate measurements and metabolite levels within the cotyledon were assessed across development to identify trends in the accumulation of central carbon and nitrogen metabolic intermediates. Active metabolic operation during late seed development was probed through transient labeling with 13C substrates. The results indicated: i) a drop in lipid during seed maturation with a concomitant increase in carbohydrates, ii) a transition from seed filling to maturation phase characterized by quantitatively balanced changes in the carbon use and CO2 release, iii) changes in measured carbon and nitrogen resources supplied maternally over development, iv) 13C metabolites processed through gluconeogenesis towards sustained carbohydrate accumulation as the maternal nutrient supply diminishes, and v) oligosaccharide biosynthetic metabolism during seed coat senescence at maturation. These results highlight temporal engineering targets for altering final biomass composition to increase the value of soybeans and a path to breaking the inverse seed protein and oil correlation. One-sentence summaryAssessment of temporal changes in metabolism during soybean seed development indicated that lipid turnover during maturation contributes carbon for gluconeogenic production of carbohydrates.

plant biology↗

bioRxiv2020

The soybean (Glycine max L.) cytokinin oxidase/dehydrogenase multigene family; identification of natural variations for altered cytokinin content and seed yield

Cytokinins (CKs) play a fundamental role in regulating dynamics of organ source/sink relationships during plant development, including flowering and seed formation stages. As a result, CKs are key drivers of seed yield. The cytokinin oxidase/dehydrogenase (CKX) is one of the critical enzymes responsible for regulating plant CK levels by causing their irreversible degradation. Variation of CKX gene activity is significantly correlated with seed yield in many crop species while in soybean (Glycine max L.), the possible associations between CKX gene family members (GFMs) and yield parameters have not yet been assessed. In this study, seventeen GmCKX GFMs were identified, and natural variations among GmCKX genes were probed among soybean cultivars with varying yield characteristics. The key CKX genes responsible for regulating CK content during seed filling stages of reproductive development were highlighted using comparative phylogenetics, gene expression analysis and CK metabolite profiling. Five of the seventeen identified GmCKX GFMs, showed natural variations in the form of single nucleotide polymorphisms (SNPs). The gene GmCKX14, with high expression during critical seed filling stages, was found to have a non-synonymous mutation (H105Q), on one of the active site residues, Histidine 105, previously reported to be essential for co-factor binding to maintain structural integrity of the enzyme. Soybean lines with this mutation had higher CK content and desired yield characteristics. The potential for marker-assisted selection based on the identified natural variation within GmCKX14, is discussed in the context of hormonal control that can result in higher soybean yield. Key MessageNatural variations in soybean cytokinin oxidase/dehydrogenase gene, GmCKX14, with high expression during seed development, were linked to increased sink strength via altered cytokinin profiles in high yielding cultivars.

plant biology↗

bioRxiv2020

Shifting carbon flux from non-transient starch to lipid allows oil accumulation in transgenic tobacco leaves

Plant leaf biomass is composed predominantly of carbohydrate and protein with less than 5% dry weight allocated to lipid and less than 1% of total lipid in the form of triacylglycerols (TAGs). The combined overexpression of multiple genes involved in different aspects of TAG synthesis and stabilization can result in TAG accumulation to over 30% dry weight in tobacco leaves, presumably requiring many metabolic adjustments within plant cells. The metabolic consequences to the combined source and sink capacities of high oil accumulating transgenic tobacco leaves compared to wild-type were inspected across development and photoperiod by utilizing foliar biomass components and 13CO2 flux through central carbon intermediates. Lipid biosynthesis was investigated through assessment of acyl-acyl carrier protein (ACP) pools using a recently derived quantification method that was extended to accommodate isotopic labeling. Lipids accumulated stepwise over plant development in the high-oil leaves, with 13CO2-labeling studies confirming increased carbon flux to lipids. The large increase in lipid content was concurrent with a decrease in foliar starch, with limited contribution from non-sucrose soluble sugars, indicating a redirection of carbon from starch to lipids. Starch accumulated non-transiently with plant age in wild-type leaves, suggesting an inherent capacity for a developmentally-regulated carbon sink in tobacco leaves that may have enabled the programmed altered carbon partitioning to lipids in transgenics. These studies provide insight into the metabolic plasticity of dual source-sink leaves over development and may in part explain recent successful leaf lipid engineering efforts in tobacco. One sentence summaryEngineering high oil accumulation in tobacco leaves is enabled by inherent source-sink plasticity associated with non-transient foliar starch accumulation over development.

plant biology↗

bioRxiv2020

PGC1/PPAR Drive Cardiomyocyte Maturation through Regulation of Yap1 and SF3B2

Cardiomyocytes undergo significant levels of structural and functional changes after birth--fundamental processes essential for the heart to produce the volume and contractility to pump blood to the growing body. However, due to the challenges in isolating single postnatal/adult myocytes, how individual newborn cardiomyocytes acquire multiple aspects of mature phenotypes remains poorly understood. Here we implemented large-particle sorting and analyzed single myocytes from neonatal to adult hearts. Early myocytes exhibited a wide-ranging transcriptomic and size heterogeneity, maintained until adulthood with a continuous transcriptomic shift. Gene regulatory network analysis followed by mosaic gene deletion revealed that peroxisome proliferator-activated receptor coactivator-1 signaling--activated in vivo but inactive in pluripotent stem cell-derived cardiomyocytes--mediates the shift. The signaling regulated key aspects of cardiomyocyte maturation simultaneously through previously unrecognized regulators, including Yap1 and SF3B2. Our study provides a single-cell roadmap of heterogeneous transitions coupled to cellular features and unveils a multifaceted regulator controlling cardiomyocyte maturation. Significance StatementHow the individual single myocytes achieve full maturity remains a black box, largely due to the challenges with the isolation of single mature myocytes. Understanding this process is particularly important as the immaturity and early developmental arrest of pluripotent stem cell-derived myocytes has emerged a major concern in the field. Here we present the first study of high-quality single-cell transcriptomic analysis of cardiac muscle cells from neonatal to adult hearts. We identify a central transcription factor and its novel targets that control key aspects of myocyte maturation, including cellular hypertrophy, contractility, and mitochondrial activity.

developmental biology↗