Pawan Kumar Raghav

Non‐classical HLA Class I genes ( HLA‐E , ‐F , ‐ G , ‐ H ) and MHC‐related genes ( MICA , MICB ) regulate NK cell function and immune tolerance, yet their allelic diversity remains underexplored. Using capture‐enriched full‐gene sequencing of 943 healthy US transplant donors, we identified 26 novel alleles across HLA‐E (2), HLA‐F (6), HLA‐G (4), HLA‐H (5), MICA (3), and MICB (6). Each novel allele was identified in a single individual and differs from its closest known allele by a single‐nucleotide polymorphism within the coding region. In addition, we confirmed the recently described allele HLA‐G*01:01:33 in 16 unrelated donors. In all cases, it consistently co‐segregated with HLA‐A*34:01:01 , HLA‐F*01:01:01 and HLA‐H*02:27 , supporting the presence of a conserved haplotype. These findings expand the catalogue of non‐classical HLA and MIC alleles, highlighting capture‐enriched full‐gene sequencing as a powerful tool for comprehensive immunogenetic profiling.

Hematopoietic stem cells (HSCs) sustain lifelong hematopoiesis through their capacity for self-renewal and multilineage differentiation. However, the isolation and functional characterization of HSCs remain challenging due to their cellular heterogeneity and dynamically regulated transcriptional and epigenetic landscapes. Advances in experimental and computational biology, including single-cell RNA sequencing (scRNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), network inference algorithms, and machine learning, have improved our ability to resolve transcriptional states, trace lineage trajectories, and reconstruct gene regulatory networks (GRN) at single-cell resolution. These approaches enable the discovery of novel HSC subtypes and regulatory factors, and facilitate the integration of multi-omics data to uncover epigenetic and transcriptional mechanisms that drive stem cell fate decisions. Additionally, machine learning models trained on high-throughput datasets provide predictive power for identifying novel enhancers, transcription factors, and therapeutic targets. This review underscores the synergistic role of computational tools in deciphering HSC biology and highlights their potential to improve stem cell therapies and precision treatments for hematologic disorders.

With their self-renewing populations (SRPs) serving as a primary reservoir within the bone marrow [1]. These cells replenish precursor populations and support the hierarchical development of myeloid, lymphoid, and erythroid intermediates, further differentiating into short-lived immune and blood cell lineages [2]. SRPs play a vital role in immune defense, contributing to responses against infections, cancer, and radiation-induced immunosuppression [3,4]. Among SRPs, longterm culture-initiating cells (LTC-ICs) are essential in restoring hematopoietic function after radiation exposure [1,2]. These cells can regenerate depleted precursor and progenitor populations, making them invaluable for treating patients exposed to high radiation doses [5].However, despite their therapeutic potential, direct transplantation of SRPs presents significant challenges due to the difficulty in mobilizing and harvesting them in sufficient quantities for clinical application [6,7]. Traditional methods, such as direct bone marrow aspiration from the iliac crest or in vitro expansion, are invasive and have logistical constraints. Meanwhile, cytokine-driven expansion requires prohibitively high doses, making it financially and biologically unfeasible. This underscores the need for novel strategies to enhance SRP availability, including gene therapy, combination treatments with cytokines and small molecules, and innovative approaches to preserving their self-renewal capacity while maintaining stemness. This research topic combines cutting-edge methodologies and protocols that advance HSC proliferation and self-renewal. This collection fosters critical discussions on optimizing SRP expansion and sustainability by consolidating insights from established and emerging techniques.As the field progresses, continued research will be essential to unlocking the full therapeutic potential of these self-renewing hematopoietic stem cells, paving the way for advancements in

Stem cell transplantation has emerged as a cornerstone of regenerative medicine due to its ability to differentiate into various cell types and its potential applications in immune modulation, treating immunological disorders, and hematological malignancies [1]. Among various stem cell types, pluripotent embryonic stem cells (ESCs) and multipotent adult stem cells (ASCs) have been extensively studied for their differentiation potential. ESCs possess superior pluripotency, allowing them to generate any cell type in the human body. However, ethical concerns surrounding their use have led to a greater focus on alternative sources such as induced pluripotent stem cells (iPSCs) and ASCs, including mesenchymal stem cells (MSCs), neural stem cells (NSCs), and hematopoietic stem cells (HSCs). MSCs have shown immunomodulatory effects by modulating T, B, natural killer (NK), and dendritic cells, making them a promising tool for autoimmune and inflammatory disorders [2,3]. HSCs from human umbilical cord blood have been widely used in transplantation therapies for hematopoietic and immune-related diseases [4]. The success of HSC transplantation (HSCT) hinges on homing, migration, engraftment, self-renewal, and differentiation. These complex processes are regulated by growth factors, cytokines, and niche interactions. Despite HSCT's therapeutic potential, challenges like graft-versus-host disease (GVHD), graft rejection, and variable patient outcomes persist. Strategies such as immune tolerance induction and genetic, and therapeutic modifications are being explored to enhance stem cell survival and integration [5][6][7][8]. Recent advancements suggest that integrating computational models with immunological data has opened new avenues to improve stem cell engraftment [9]. Machine learning models enable the identification of key transcription factors and gene networks involved in self-renewal and lineage specification in regenerative medicine [10,11]. These approaches also facilitate the comparison of healthy stem cells and cancer stem cells (CSCs), aiding in the development of targeted therapies for malignancies [12,13].The Frontiers in Immunology research topic, "Improving Stem Cell Transplantation Delivery Using Computational Modelling" exemplifies this interdisciplinary approach and brings together pioneering studies in a series of compiled articles, contributing unique insights into the field. A Additionally, the development of computational tools to monitor stem cell transplantation progress in real time will allow for timely interventions. The articles within this research topic exemplify the transformative potential of computational modeling in stem cell transplantation. By integrating advanced computational tools with experimental and clinical data, these studies pave the way for personalized and more effective therapeutic strategies, ultimately enhancing patient outcomes in stem cell transplantation.

Stem cell transplantation has proven effective in treating acute and chronic wounds, but its limitations, such as low cellular viability and the need for specialized transportation, highlight the necessity for alternative approaches. This review explores the potential of engineered exosomes, containing identified miRNAs/peptides, as a more stable and efficient cell-free therapy for regenerative medicine, particularly in wound healing. The discussion emphasizes the benefits of exosomes, including their stability, reduced damage, and consistent biological activity, paving the way for innovative applications like lyophilized exosomes, mist spray delivery, and exosome-based scaffolds. The exploration of cell-free therapy in this review holds promising implications for advancing wound-healing strategies.

The high magnitude zoonotic event has caused by Severe Acute Respitarory Syndrome CoronaVirus-2 (SARS-CoV-2) is Coronavirus Disease-2019 (COVID-19) epidemics. This disease has high rate of spreading than mortality in humans. The human receptor, Angiotensin-Converting Enzyme 2 (ACE2), is the leading target site for viral Spike-protein (S-protein) that function as binding ligands and are responsible for their entry in humans. The patients infected with COVID-19 with comorbidities, particularly cancer patients, have a severe effect or high mortality rate because of the suppressed immune system. Nevertheless, there might be a chance wherein cancer patients cannot be infected with SARS-CoV-2 because of mutations in the ACE2, which may be resistant to the spillover between species. This study aimed to determine the mutations in the sequence of the human ACE2 protein and its dissociation with SARS-CoV-2 that might be rejecting viral transmission. The in silico approaches were performed to identify the impact of SARS-CoV-2 S-protein with ACE2 mutations, validated experimentally, occurred in the patient, and reported in cell lines. The identified changes significantly affect SARS-CoV-2 S-protein interaction with ACE2, demonstrating the reduction in the binding affinity compared to SARS-CoV. The data presented in this study suggest ACE2 mutants have a higher and lower affinity with SARS-Cov-2 S-protein to the wild-type human ACE2 receptor. This study would likely be used to report SARS-CoV-2 resistant ACE2 mutations and can be used to design active peptide development to inactivate the viral spread of SARS-CoV-2 in humans.