Biology subjects

Aggarwal, V.

Publications and source records attributed to Aggarwal, V..

6 recordsLinked to original sources

bioRxiv2026

Role of brown adipose tissue-specific sympathetic and sensory innervations in menthol-induced energy-expending phenotype

Menthol, a TRPM8 agonist and pharmacological cold mimic, activates brown adipose tissue (BAT) to increase adaptive thermogenesis and whole-body energy expenditure. BAT is highly innervated with sensory and sympathetic nerve fibers. There is existing literature that sympathetic nerves have a critical role in regulating menthols (or cold) effect on energy homeostasis, but there is minimal literature about the involvement of somatosensory innervations. Here, we ought to investigate whether short-term topical application (3 days) of menthol elicits sympathetic and sensory nerves. We used chemical denervation models of complete (100 mg/kg; 6-OHDA and 125 mg/kg; capsaicin, s.c.) and localized (in BAT, 20 {micro}L of 6-OHDA (20 mg/ml) and 20{micro}L of capsaicin (20{micro}g/{micro}L) in each BAT lobe) ablation of sympathetic (6-OHDA) and sensory (capsaicin) innervations to explore their roles in menthol induced BAT activation and energy expanding phenotype. In the present study we have shown that (i) short-term topical application of menthol (10 % menthol for 3 consecutive days) elicits sympathetic and sensory innervations, and induces thermogenesis, lipolysis and mitochondrial biogenesis in mice BAT; (ii) localized ablation of sympathetic innervation prevented menthol induced BAT activation and lipolysis (iii) localized ablation of sensory neurons augments sympathetic innervations induced BAT activation and modulated thermoregulation. Collectively, our results suggest that BAT activation following short-term topical application of menthol is dependent primarily but not exclusively on centrally mediated SNS activation. TRPV1-positive sensory neurons are partially necessary for thermoregulation and efficient BAT activation.

pharmacology and toxicology↗

bioRxiv2025

AnnotateAnyCell: Open-Source AI Framework for Efficient Annotation in Digital Pathology

AO_SCPLOWBSTRACTC_SCPLOWManual annotation of histopathological whole slide images remains a critical bottleneck for computational pathology and clinical AI deployment, requiring prohibitive expert time at scale. Here we present an open-source semi-supervised framework combining active contrastive learning with iterative human-in-the-loop feedback for efficient cellular annotation and classification. The pipeline integrates Cellpose segmentation, UMAP-based latent space visualization, and contrastive learning with pseudolabel propagation, evaluated on five whole slide images of canine invasive urothelial carcinoma across low, intermediate, and high histological grades at 40x magnification. Latent space clustering-guided annotation required 47 minutes compared to 63 minutes for sequential annotation, a 25% reduction (95% CI 18-32%). Classification accuracy reached 96.3% {+/-} 1.2% for mitotic figures and 98.3% {+/-} 1.4% for nucleoli using 1,075 labeled samples, with nucleoli classification achieving 95.5% {+/-} 1.5% accuracy from only 215 samples. Inter-annotator agreement was high for chromatin ({kappa} = 1.00) and nucleoli ({kappa} = 0.95) but moderate for mitotic figures ({kappa} = 0.58) and nuclear shape ({kappa} = 0.36), reflecting intrinsic morphological ambiguity in these categories. This framework substantially reduces annotation burden while achieving expert-level accuracy for well-defined morphological features, providing a scalable path toward AI-assisted diagnostics in resource-constrained pathology settings.

pathology↗

bioRxiv2024

Protrusion force and cell-cell adhesion play a critical role in collective tumor cluster migration

Collective migration refers to the coordinated movement of cells as a single unit during migration. While collective migration enhances invasive and metastatic potential in cancer, the mechanisms driving this behavior and regulating tumor migration plasticity remain poorly understood. This study provides a mechanistic model explaining the emergence of different modes of collective migration under hypoxia-induced secretome. We focus on the interplay between cellular protrusion force and cell-cell adhesion using collectively migrating three-dimensional microtumors as models with well-defined microenvironments. Large microtumors show directional migration due to intrinsic hypoxia, while small microtumors exhibit radial migration when exposed to hypoxic secretome. Here, we developed an in silico multi-scale microtumor model (MSMM) based on the cellular Potts model and implemented in CompuCell3D to elucidate underlying mechanisms. We identified distinct migration modes within specific regions of protrusion force and cell-cell adhesion parameter space and studied these modes using in vitro experimental microtumor models. We show that sufficient cellular protrusion force is crucial for radial and directional collective microtumor migration. Radial migration emerges when sufficient cellular protrusion force is generated, driving neighboring cells to move collectively in diverse directions. Within migrating tumors, strong cell-cell adhesion enhances the alignment of cell polarity, breaking the symmetric angular distribution of protrusion forces and leading to directional microtumor migration. The integrated results from the experimental and computational models provide fundamental insights into collective migration in response to different microenvironmental stimuli. Our computational and experimental models can adapt to various scenarios, providing valuable insights into cancer migration mechanisms. Statement of SignificanceWhile single-cell metastasis is well-studied, mechanisms of collective cluster migration are less understood. Significant challenges include the lack of a fundamental physics perspective on collective cluster migration mechanisms and suitable physiologically relevant three-dimensional (3D) in vitro models that can recapitulate collective cluster migration. In this article, we developed a computational model depicting microtumor migration behaviors. Collective cell migration, with varying correlation lengths, exhibits different migratory modes such as directional and radial migration. These modes are predicted by in silico models and confirmed using experimental microtumor models. Machine learning methods were exploited to identify migratory modes. Our computational and experimental models are flexible in various circumstances, offering insights into cancer migration mechanisms.

cancer biology↗

bioRxiv2022

The START domain potentiates HD-ZIPIII transcriptional activity

HD-ZIPIII transcription factors (TFs) were repeatedly deployed over 725 million years of evolution to regulate central developmental innovations. The START domain of this pivotal class of developmental regulators was recognized over twenty years ago, but its putative ligands and functional contributions remain unknown. Here, we demonstrate that the START domain promotes HD-ZIPIII TF homodimerization and increases transcriptional potency. Effects on transcriptional output can be ported onto heterologous TFs, consistent with principles of evolution via domain capture. We also show the START domain binds several species of phospholipids, and that mutations in conserved residues predicted to affect either ligand binding, or its downstream readout, abolish HD-ZIPIII DNA-binding competence. Our data present a model in which the START domain potentiates transcriptional activity and uses ligand-induced conformational change to render HD-ZIPIII dimers competent to bind DNA. These findings resolve a long-standing mystery in plant development and highlight the flexible and diverse regulatory potential coded within this widely distributed evolutionary module.

plant biology↗

bioRxiv2022

P4HA2: A link between tumor-intrinsic hypoxia, partial EMT and collective migration

Epithelial-to-mesenchymal transition (EMT), a well-established phenomenon studied across pan-cancer types, has long been known to be a major player in driving tumor invasion and metastasis. Recent studies have highlighted the importance of partial EMT phenotypes in metastasis [1]. Initially thought as a transitional state between epithelial and mesenchymal phenotypic states, partial EMT state is now widely recognized as a key driver of intra-tumoral heterogeneity and phenotypic plasticity, further accelerating tumor metastasis and therapeutic resistance. However, how tumor microenvironments regulate partial EMT phenotypes remains unclear. We have developed unique size-controlled three-dimensional microtumor models that recapitulate tumor-intrinsic hypoxia and the emergence of collectively migrating cells. In this study, we further interrogate these microtumor models to understand how tumor-intrinsic hypoxia regulates partial EMT and collective migration in hypoxic large microtumors fabricated from T47D breast cancer cells. We also compared global gene expression profiles of hypoxic, migratory microtumors to that of non-hypoxic, non-migratory microtumors at early and late time-points. Using our microtumor models, we identified unique gene signatures for tumor-intrinsic hypoxia (early versus late), partial EMT and migration (pre-migratory versus migratory phenotype). Through differential gene expression analysis between the microtumor models with an overlap of hypoxia, partial EMT and migration signatures, we identified prolyl 4-hydroxylase subunit 2 (P4HA2), a hypoxia responsive gene, as a central regulator common to hypoxia, partial EMT and collective migration. Further, the inhibition of P4HA2 significantly blocked collective migration in hypoxic microtumors. Thus, using the integrated computational-experimental analysis, we identify the key role of P4HA2 in tumor-intrinsic hypoxia-driven partial EMT and collective migration.

cancer biology↗

bioRxiv2020

Systematic evaluation of genome sequencing as a first-tier diagnostic test for prenatal and pediatric disorders

Current clinical guidelines recommend three genetic tests for the assessment of fetal structural anomalies: karyotype to detect microscopically-visible balanced and unbalanced chromosomal rearrangements, chromosomal microarray (CMA) to detect sub-microscopic copy number variants (CNVs), and exome sequencing (ES) to identify individual nucleotide changes in coding sequence. Advances in genome sequencing (GS) analysis suggest that it is poised to displace the sequential application of all three conventional tests to become a single diagnostic approach for the assessment of fetal structural anomalies. However, systematic benchmarking is required to assure that GS can capture the full mutational spectrum associated with fetal structural anomalies and to accurately quantify the added diagnostic yield of GS. We applied a novel GS analytic framework that included the discovery, filtration, and interpretation of nine classes of genomic variation to 7,195 individuals. We assessed the sensitivity of GS to detect diagnostic variants (pathogenic or likely pathogenic) from three standard-of-care tests using 1,612 autism spectrum disorder quartet families (ASD; n=6,448) with matched GS, ES, and CMA data, and validated these findings in 46 fetuses with a clinically reportable variant originally identified by karyotype, CMA, or ES. We then assessed the added diagnostic yield of GS in 249 trios (n=747) comprising a fetus with a structural anomaly detected by ultrasound and two unaffected parents that were pre-screened with a combination of all three standard-of-care tests. Across both cohorts, our GS analytic framework identified 98.2% of all diagnostic variants detected by standard-of-care tests, including 100% of those originally detected by CMA (n=88) and ES (n=61), as well as 78.6% (n=11/14) of the chromosomal rearrangements identified by karyotype. The diagnostic yield from GS was 7.8% across all 1,612 ASD probands, almost two-fold more than CMA (4.4%) and three-fold more than ES (3.0%). We also demonstrated that the yield of ES can approach that of GS when CNVs are captured with high sensitivity from exome data (7.4% vs. 7.8%, respectively). In 249 pre-screened fetuses with structural anomalies, GS provided an additional diagnostic yield of 0.4% beyond the combination of all three tests (karyotype, CMA, and ES). Applying our benchmarking results to existing data indicates that GS can achieve an overall diagnostic yield of 46.1% in unselected fetuses with fetal structural anomalies, providing an estimated 17.2% increase in diagnostic yield over karyotype, 14.1% over CMA, and 36.1% over ES when sequence variants are assessed, and 4.1% when CNVs are also identified from exome data. In this study we demonstrate that GS is sensitive to the detection of almost all pathogenic variation captured by karyotype, CMA, and ES, provides a superior diagnostic yield than any individual test by a wide margin, and contributes a modest increase in diagnostic yield beyond the combination of all three tests. We also outline several strategies to aid the interpretation of GS variants that are cryptic to conventional technologies, which we anticipate will be increasingly encountered as comprehensive variant identification from GS is performed. Taken together, these data suggest GS warrants consideration as a first-tier diagnostic approach for fetal structural anomalies.

genomics↗