Biology subjects

Malik, P. S.

Publications and source records attributed to Malik, P. S..

7 recordsLinked to original sources

bioRxiv2025

Dual Preconditioned Mesenchymal Stem Cells-Derived Culture Conditioned Media Augment Immunomodulation and Drive Metabolic Reprogramming in Acute Graft-versus-Host Disease

BackgroundThe toxicity associated with conventional conditioning regimens limits treatment outcomes in autoimmune disorders such as acute graft-versus-host disease (aGVHD). Developing effective adjunctive interventions to restore immune balance is therefore essential. This study investigated the immunomodulatory potential of mesenchymal stem cells (MSCs)-derived culture-conditioned media (CCM) from naive and preconditioned MSCs-hypoxic (MSCsHYP), apoptotic (MSCsAPO), and dual preconditioned (MSCsHYP+APO) in aGVHD. MethodsHuman MSCs isolated from bone marrow and Whartons Jelly were preconditioned under hypoxia (1% O2), apoptosis (1 {micro}M staurosporine, 24 h), or both. The immunoregulatory and antioxidant properties of their CCM were evaluated through T-cell proliferation assays, Treg induction, macrophage polarization, mitochondrial function, and T-cell bioenergetics. Comparative proteomic profiling of CCM from WJ-MSCs and WJ-MSCsHYP+APO co-cultured with aGVHD patient-derived activated PBMNCs was performed using LC-MS/MS, alongside in vivo validation in a chemotherapy-induced aGVHD murine model. ResultsWJ-MSCsHYP+APO-CCM exhibited superior immunomodulatory efficacy, suppressing T-cell proliferation, promoting Treg and Th2/Th9 differentiation, and driving M2 macrophage polarization. It reduced mitochondrial ROS, enhanced mitochondrial polarization, and shifted T-cell metabolism from glycolysis toward oxidative phosphorylation. Proteomic analysis revealed modulation of IL-12, IL-17, and JAK-STAT pathways, along with regulation of complement, coagulation, and metabolic cascades. Interaction with immune cells further enhanced its antioxidant and tissue-reparative properties via extracellular matrix remodeling. ConclusionDual preconditioning under hypoxia and apoptosis amplifies the immunomodulatory, antioxidant, and reparative efficacy of WJ-MSC-CCM, offering a potent non-cellular therapeutic strategy for aGVHD management. GRAPHICAL ABSTRACTImmunomodulatory and immune metabolic reprogramming potential of naive and preconditioned MSCs (MSCs, MSCsHYP, MSCsAPO, MSCsHYP+APO) derived CCM in Acute GVHD (created using Biorender.com) O_FIG O_LINKSMALLFIG WIDTH=95 HEIGHT=200 SRC="FIGDIR/small/686182v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f7ed73org.highwire.dtl.DTLVardef@1063015org.highwire.dtl.DTLVardef@13ed827org.highwire.dtl.DTLVardef@e103c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Predictive Modeling of acute Graft-versus-Host-Disease using Machine Learning on Immune Cell and Cytokine Profiles at Engraftment

BackgroundAcute Graft-versus-Host-Disease (aGvHD) is a major immune complication following allogenic hematopoietic stem cell transplantation (Allo-HSCT), initiated by conditioning regimen-associated tissue damage. It involves the complex interplay of immune cells and cytokines. Our study aims to leverage machine learning (ML) algorithms on the immune and cytokine profile of Allo-HSCT recipients to develop biomarker-based classification models to predict the onset of aGvHD at the time of engraftment. Materials and MethodsSeventy patients diagnosed with hematological disorders who had undergone Ist Allo-HSCT were recruited from All India Institute of Medical Sciences, New Delhi, India. Peripheral blood (PB) was collected from the patients at the time of engraftment, and the immune cell subtypes and cytokine profiles were analyzed using flow cytometry and ELISA respectively. The individual cell counts were then processed using basic ML models, including support vector classifier with RBF kernel, Decision Tree, and Random Forest, chosen for their mathematical simplicity and feature importance advantage of Decision Trees and Random Forests. Various data settings were utilized in the study: combined immune and cytokine counts, immune cell counts only, cytokine counts only, T-cell counts only, NK cell counts only, dendritic cell counts only, and B-cell counts only. These configurations were selected to investigate how different data sets impact the prediction of aGvHD before its onset. ResultsAt the engraftment flow cytometric analysis of reconstituted lymphocytes in patients who developed aGvHD revealed that there was a remarkable decrease in the ratio of CD4+/CD8+ T-cell and Tregs, with an increase in the cytotoxic regulatory NK-cell, dendritic cells, and B-cell. The levels of pro-inflammatory cytokines (IFN-{gamma}, IL-1{beta}, IP-10, TNF-, IL-17, IL-12p70, MIP-1, MIP-1{beta}, RANTES), and Th17-and Th1-cells were elevated with consequent decline of the levels of anti-inflammatory cytokine IL-10, IL-2, IL-4 and Th2-, Th9-cells. Machine learning based on 48 parameters [all immune cell subsets n=34 and all cytokines (n=14)]. The correlation heat map shows a higher correlation of aGvHD with the cytokine profile with or without immune cells (accuracy: 1), T-cell alone (accuracy: 0.96); NK-cell alone (accuracy: 0.93); dendritic cells alone (accuracy: 0.90), B-cell alone (accuracy: 0.86). ConclusionThe current models classify perfectly, indicating the potential for a ML algorithm in predicting the onset of aGvHD. However, a study with a larger sample size is required to validate these classification models and mitigate the risk of overfitting observed due to the consistently high performance. The study also highlights the potential of cytokine profiles as a viable alternative to T-cell counts, as evidenced by the correlation heat map and classifier models. These findings provide valuable insights into dataset requirements and future directions for integrating ML models into aGvHD prediction.

immunology↗

bioRxiv2025

Hypoxia-Mediated Molecular Interactions of Tissue-Specific Mesenchymal Stem Cells Drive Metabolic Reprogramming and Immunomodulation in Acute Graft-versus-Host Disease

BackgroundMesenchymal stem cells (MSCs) mediate immunomodulation through various mechanisms, including apoptosis, efferocytosis, and mitochondrial transfer. Our study investigates the impact of hypoxia preconditioning on the immune metabolic reprogramming and immunomodulatory potential of MSCs in acute graft-versus-host disease (aGVHD). Additionally, we explored the differential immunomodulatory effects of tissue-specific MSCs, specifically bone marrow (BM) and Whartons Jelly (WJ), and elucidated the mechanisms underlying variability in their therapeutic efficacy. MethodsMSCs were isolated from BM and WJ and subjected to hypoxia preconditioning. Their immunometabolic programming potential was assessed by evaluating T-cell proliferation, regulatory T-cell (Treg) induction, effector T-cell differentiation toward Th2, Th9 phenotypes, and macrophage polarization, T-cell bioenergetics in the direct co-culture systems. ResultsWJ-MSCsHYP exhibited superior immunomodulatory properties compared to BM-MSCsHYP, by inhibiting T-cell proliferation, enhancing Treg induction, and promoting anti-inflammatory macrophage polarization. WJ-MSCsHYP demonstrated enhanced mitochondrial transfer to T-cell, improving mitochondrial health, reducing ROS, and promoting oxidative phosphorylation, leading to immune homeostasis. Unlike BM-MSCs, WJ-MSCs exhibited higher rates of apoptosis, which facilitated immune modulation through mechanisms independent of efferocytosis. ConclusionOur findings highlight that WJ-MSCsHYP is a superior candidate for aGVHD by utilizing apoptosis, mitochondrial transfer, and metabolic reprogramming to achieve immune regulation.

immunology↗

bioRxiv2024

Microarray Integrated Spatial Transcriptomics (MIST) for Affordable, Robust, and Comprehensive Digital Pathology

10X Visium, a popular Spatial transcriptomics (ST) method, faces limited adoption due to its high cost and restricted sample usage per slide. To address these issues, we propose Microarray Integrated Spatial Transcriptomics (MIST), combining conventional tissue microarray (TMA) with Visium, using laser-cutting and 3D printing to enhance slide throughput. Our design facilitates independent replication and customization in individual labs to suit specific experimental needs. We provide a step-by-step guide from designing TMAs to the library preparation step. We demonstrate MISTs cost-effectiveness and technical benefits over Visium and GeoMx Nanostring. We also introduce AnnotateMap, a novel computational tool for efficient analysis of multiple ROIs processed through MIST.

bioinformatics↗

bioRxiv2024

Synergistic Hypoxia and Apoptosis Conditioning Unleashes Superior Mesenchymal Stem Cells Efficacy in Acute Graft-versus-Host-Disease

Mesenchymal stem cells (MSCs) have emerged as promising candidates for immune modulation in various diseases that are associated with dysregulated immune responses like Graft-versus-Host-Disease (GVHD). MSCs are pleiotropic and the fate of MSCs following administration is a major determinant of their therapeutic efficacy. In this context, we here demonstrate that hypoxia preconditioned apoptotic MSCs [bone marrow (BM), Whartons Jelly (WJ)] bear more immune programming ability in a cellular model of acute Graft-versus-Host-Disease (aGVHD). To this purpose, we programmed MSCs by exposing them to hypoxia and inducing apoptosis both sequentially as well as simultaneously. Our findings demonstrated that WJ MSCs that were conditioned with indicated approaches simultaneously induced the differentiation of CD4+T-cell towards Tregs, enhanced Th2 effector, and concomitantly mitigated Th1 and Th17, with polarization of M1 effector macrophages towards their M2 phenotype, and more interestingly enhanced efferocytosis by macrophages indicated Th2 programming ability of MSCs programmed by conjunctional approaches Overall, our study highlights the potential of WJ-MSCs conditioned with hypoxia and apoptosis concurrently, as a promising therapeutic strategy for aGVHD and underscores the importance of considering MSC apoptosis in optimizing MSCs-based cellular therapy protocols for enhanced therapeutic efficacy in aGvHD. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=123 HEIGHT=200 SRC="FIGDIR/small/588248v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@92c102org.highwire.dtl.DTLVardef@bd895forg.highwire.dtl.DTLVardef@185e041org.highwire.dtl.DTLVardef@45ef0f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

DeepMPS: Development and validation of a deep learning model for whole slide image base prognostic prediction of low grade Lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is one of the most common cancers, and patients prognostication is crucial for treatment decisions. Histopathological images are the most generally accessible clinical information, however they have not been employed in clinical settings for prognosis. In this study, we used WSIs and clinical data from TCGA (training and testing) and East Asian Cohort (EAS, Validation) to develop and validate DL-based prognosticator. To circumvent the need for manual ROI generation, WSIs from these patients were divided into smaller patches and scored. DeepMPS prediction model was built using the top scoring 226,383 patches. The DeepMPS model showed a C-index of 0.638 in the TCGA training cohort. The univariate and multivariate cox regression analysis identified DeepMPS as an independent predictor of survival (HR: 9.48, p-value: <0.0001) in the training cohort. The training cohort of patients was separated into low and high-risk groups at various points. Kaplan-Meier analysis showed the highest difference in survival of low and high-risk patients at the 75th percentile (HR: 3.58, 95% CI: 2.57-5.00, p-value: <0.0001). At the same cut-off as the training samples, TCGA testing cohort patients demonstrated a significant difference in survival when split into low and high-risk patients (HR: 2.30, 95% CI: 1.11-4.82, p-value: 0.044). The DeepMPS model was validated in the EAS cohort patients. DeepMPS risk score significantly segregated EAS patients into low and high-risk groups at the same cut-off point as the training cohort (HR: 2.09, 95% CI: 1.11-3.97, p-value: 0.008). In multivariate Cox regression analysis, the DeepMPS score outperformed the stage in survival prediction. We also compared the DeepMPS model with the previously developed DL-based model to show that it was the best predictor of survival with the highest C-index. In conclusion, we developed a robust DL-based prognostic model which can predict the LUAD outcome without manual intervention using histopathological images. Author SummaryRight from the initial step of cancer diagnosis, histopathological images are the widely relied source of information for therapeutic decision making. Though these images carry a substantial amount of information, their use remains restricted to determining the grade of the tumor by pathologists. The advent of computational techniques has given rise to the ability to capture vital information besides the grade of the tumor, which humans might not be able to quantify visually. To this end, this work proposes a deep learning based model, Deep Multi-Modal Prognosis System (DeepMPS), to predict the prognosis of the patients based on the histopathological images to provide additional information to the pathologist which will aid in clinical decision making. DeepMPS predicts a risk score associated with each individual based on histopathological images and clinical factors. The risk score obtained from the proposed system is an independent predictor of survival. As the proposed system is independent of the manual region-of-interest (RoI) generation, it will ease the pathologists workload.

pathology↗

bioRxiv2022

Spurious off-target signals from potential lncRNAs by 10X Visium probes

Spatial transcriptomics has revolutionized molecular profiling of tissues in a spatial context, especially in the study of cancer heterogeneity. 10X Genomics facilitates spatial gene expression profiling platforms to help work with fresh-frozen (FF) and formalin fixed paraffin embedded (FFPE) tissues. FF analysis is based on polyA capture of RNAs while FFPE analysis uses a pre-designed set of probes to capture transcripts of coding genes. Previously, we used FFPE spatial data as a negative control in a study to identify novel non-coding RNAs in FF data. Interestingly, we find and report that certain target probes used in FFPE show off-target signals from lncRNAs. The Space Ranger pipeline of 10X Visium counts the expression of these potential off-targets to be that of the corresponding target gene, some of which have known implications in cancer and its diagnosis. Therefore, relying on this technology is not ideal to investigate expression of the genes reported in this study. We hereby recommend excluding those genes in any downstream analysis of FFPE datasets and to design probes with better specificity, considering the sequence similarity between genes and non-coding RNAs.

bioinformatics↗