Transplantation

Background: We aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients. Methods: We studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients. Findings: Four distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine month graft loss (72.3%, 31.1%, 2.2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0.85) and consistent class assignment and trajectory patterns when applied to CTOT-20. Interpretation: We identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.

Background: Primary graft dysfunction (PGD) remains one of the leading causes of early mortality after pediatric heart transplant (HT). While neurodevelopmental impacts of congenital heart disease (CHD) are well-characterized, the effect of PGD on long-term neurodevelopmental outcomes in pediatric HT recipients remains unknown. We sought to determine the association between PGD and neurodevelopmental outcomes in this population. Methods: We performed a retrospective cohort study using the United Network for Organ Sharing (UNOS) database. All pediatric (age <18 years) isolated heart transplant recipients from 2010-2025 were included. The most recent pre- and post-transplant neurodevelopmental outcomes including cognitive delay, motor development, academic progress, and function status (stratified by age) were compared between PGD (n=434) and non- PGD groups (n=6956). Results: PGD patients had significantly worse pre-transplant functional status and motor development. Post-transplant, PGD was associated with worse motor development (18.8% vs. 13.0% definite motor delay; p=0.01) and functional status in younger children (39.5% vs. 57.8% able to keep up with peers; p<0.001). Post-transplant stroke occurred 3.5 times more frequently in PGD patients (11.5% vs. 3.3%; p<0.001). Cognitive development (p=0.94) and academic progress (p=0.096) did not differ significantly. Thirty-day (7.8% vs. 1.9%) and 1-year mortality (20.3% vs. 6.4%) were significantly higher in PGD patients (both p<0.001). Conclusions: This is the first study to characterize neurodevelopmental outcomes in pediatric patients undergoing HT with PGD. PGD is associated with significantly worse motor development and functional status independent of pre-transplant baseline. There is a 3.5-fold higher stroke rate providing a plausible neurological mechanism. The findings support targeted developmental surveillance recommendations and early intervention for this high-risk population.

Background: Kidney transplantation is the preferred treatment strategy for end-stage kidney disease. Deceased donor kidneys usually undergo cold storage until kidney transplantation, leading to cold ischemia injury that may contribute to poor graft outcomes. However, the molecular characterization of potential mechanisms of cold ischemia injury remains incomplete. Results: To bridge this knowledge gap, we leveraged the 10x Visium spatial transcriptomic technology to perform full transcriptome profiling of murine kidneys subject to varying durations of cold ischemia typical in a deceased donor kidney transplant setting. We developed a computational workflow to identify and compare spatiotemporal transcriptomic changes that accompany the injury pathophysiology in a tissue compartment-specific manner. We identified proportional enrichment of oxidative phosphorylation (OXPHOS) genes with increasing duration of cold ischemia injury within the oxygen-lean inner medulla region, suggestive of atypical metabolic presentation. This was distinct in cold ischemia injury tissue compared to warm ischemia-reperfusion kidney injury tissue. Spatiotemporal trends were validated by qPCR and immunofluorescence in a larger cohort of mice. We provide an interactive online browser at https://jef.works/CellCarto-ColdIschemia/ to facilitate exploration of our results by the broader scientific and clinical community. Conclusions: Altogether, our spatiotemporal transcriptomic analysis identified coordinated molecular changes within metabolic pathways such as OXPHOS deep within the cold ischemic kidney, highlighting the need for increased attention to the inner medulla and potential opportunities for new insights beyond those available from superficial biopsy-focused tissue examinations.

The intestinal microbiome influences immune recovery and long-term outcomes after allogeneic hematopoietic stem cell transplantation (allo-SCT). While reduced bacterial diversity and depletion of immunomodulatory microbial metabolites during peri-engraftment have been linked to acute graft-versus-host disease (aGvHD) and mortality, it remains unclear whether microbiome recovery after engraftment and immune reconstitution is better reflected by bacterial diversity or by microbial metabolic output. We aimed to define microbiome recovery in the late post-transplant period and test whether a metabolite-based biomarker improves the prediction of clinical outcomes, including overall survival (OS) and chronic (c) GvHD. In this two-center longitudinal observational study, serial stool samples were collected from pre-transplant baseline to day +100 after allo-SCT in a discovery cohort (n = 20, Technical University Munich University Hospital (TUM)) and an independent validation cohort (n = 100, University Hospital Regensburg (UKR)). Gut microbiome composition was assessed by 16S rRNA gene amplicon sequencing, with metagenomic profiling in selected patients, and stool metabolites were quantified using targeted mass spectrometry. Patients were classified as RECOVERY or NO RECOVERY based on changes in bacterial richness between baseline and the post-transplant period. To capture microbial metabolic output, the previously established Immune-Modulatory Metabolite Risk Index (IMM-RI), comprising butyric, propionic, and isovaleric acids, desaminotyrosine and indole-3-carboxaldehyde, was adapted to the late post-transplant period (IMM-RI post-TX). Bacterial alpha diversity frequently improved by day +100; however, this did not consistently indicate restoration of baseline community structure and was not paralleled by recovery of stool metabolite profiles. Accordingly, RECOVERY status showed a limited association with survival or transplant-related mortality (TRM). In contrast, IMM-RI post-TX low-risk identified patients with preserved butyrate-associated biosynthetic capacity and was significantly associated with improved OS in both cohorts (UKR: HR 0.2052, 95% CI 0.07703 - 0.5466, p < 0.0001). In the validation cohort, IMM-RI post-TX low-risk was significantly associated with reduced relapse-related mortality. Interestingly, stool butyric-, propionic and valeric acid concentrations were increased in cGvHD of the skin, indicating context-dependent metabolite effects. These findings suggest that metabolite profiling outperforms bacterial diversity for predicting outcomes after allo-SCT and support microbial metabolites as promising biomarkers for risk stratification and actionable candidates for precision microbiome interventions after allo-SCT.

Current clinical management of multiple myeloma (MM) relies on bone marrow (BM) biopsies for minimal residual disease (MRD) assessment. While BM biopsies are the gold standard, their invasive nature and potential to miss extramedullary or patchy disease necessitate sensitive, non-invasive liquid biopsy platforms. In this study, we evaluated the analytical performance of the CellSearch CMMC assay to determine its utility for deep-MRD monitoring. Using a standard 4 mL whole blood input, the assay achieves a WBC-normalized sensitivity of 2.45 x 10-7, supported by a limit of quantitation of 5 cells per run. Given this high analytical sensitivity, the assay provides a robust negative predictive value, rendering false-negative findings highly unlikely in populations with detectable peripheral disease. These findings characterize the CellSearch CMMC assay as a highly sensitive, analytically validated platform for non-invasive deep-MRD level longitudinal surveillance monitoring. When integrated into a clinical workflow that accounts for its specificity profile, the platform offers a patient-friendly complement to serial BM biopsies, with the potential to reduce their frequency in appropriate clinical contexts.

Background: Renal impairment is associated with increased risk of Parkinson's disease (PD) in general populations; however, the renal-PD link within cardiovascular disease (CVD) patients remains unclear through the high comorbidity of renal dysfunction and elevated PD risk among this special population. Objectives: To assess renal function's association, longitudinal trajectories and predictive value for PD specifically within a cardiovascular disease cohort. Methods: Among 29,266 UK Biobank CVD patients, we assessed baseline renal function via creatinine-based (eGFRcr) and cystatin C-based (eGFRcys) estimated glomerular filtration. Multivariable Cox regression analyzed associations with incident PD and all-cause mortality, with wide sensitivity analyses addressing reverse causation/confounding. Nested case-control analysis characterized pre-PD eGFR trajectories over 14 years. We finally evaluated whether renal function improved the PREDICT-PD's predictive ability. Results: Over a median 13.1-year follow-up, 489 incident PD cases and 5,919 deaths occurred. Lower eGFR levels exhibited dose-dependent associations with increased PD risk (eGFRcr: HR=0.87 [0.80~0.95]; eGFRcys: HR=0.90 [0.82~0.99]) and all-cause mortality (eGFRcr: HR=0.77 [0.75~0.79]; eGFRcys: HR=0.64 [0.63~0.66]). Pre-PD eGFR trajectories diverged significantly from controls starting over 14 years before diagnosis. eGFR-defined chronic kidney disease (<60 ml/min/1.73m2) conferred 38~60% higher PD risk and 159~234% elevated mortality risk, and could significantly enhance PREDICT-PD's discrimination, with a 1.18~1.34% increase in prediction accuracy. Conclusions: Impaired renal function is an independent PD and all-cause mortality risk factor of CVD patients, preceded by a slow, progressive eGFR decline starting >14 years before diagnosis. Incorporating renal function substantially improves PD risk prediction in this population.

Chimeric antigen receptor (CAR) T cell therapy utilizes genetically engineered patient-derived T cells to target cancer cells. Despite its clinical successes in multiple cancer types, the underlying molecular mechanisms by which molecules on CAR-T cells and surrounding cells interact with other proteins and collectively determine treatment efficacy remain elusive. Most previous studies have relied on transcriptome profiling, which does not fully reflect protein-level organization and interactions. In this study, we developed an antibody-oligonucleotide conjugate targeting the FMC63 region of CAR and integrated it into molecular pixelation (MPX). This approach enabled profiling of the dynamics of CAR molecules on cell surfaces as well as their colocalization with other proteins at the single-cell level. By applying MPX to longitudinal samples from three patients undergoing CAR-T cell therapy, we characterized the dynamic changes in CAR-associated protein organization in both pre-infusion CAR products and post-infusion peripheral blood. While CAR protein abundance and polarization showed limited variation across clinical courses, remodeling of a CAR-centered co-localization network was observed over time, including different retentions of specific molecular associations between patients with different clinical outcomes. Although derived from a limited cohort, our study identifies insights from this methodological framework beyond those gained by conventional omics analyses and offers results of a systematic investigation to predict and enhance CAR therapeutic outcomes. Key pointsO_LIMolecular pixelation was applied for chimeric antigen receptor (CAR) profiling at single-molecule and single-cell resolutions. C_LIO_LIProtein and transcriptome analyses of the CAR molecule showed dynamic remodeling during CAR-T therapy in patients with non-Hodgkin lymphoma. C_LI

Waldenstrom macroglobulinemia (WM) is a rare indolent neoplasm characterized by presence of more than 10% lymphoid cells in BM that exhibit plasmacytoid or plasma cell differentiation that secretes an IgM monoclonal protein. This is a retrospective analysis of 89 patients of WM that describes the clinical and laboratory characteristics, treatment patterns and outcome of patients of WM. The median age of the entire cophort was 66 years with male predominance (67.4%). Most common presentations were symptoms pertaining to anemia (77.5%) and constitutional symptoms (33.7%). Median bone marrow lymphoplasmacytic cells were 41%. Positivity for MYD88 and CXCR4 mutations were seen in 81.8% and 2.4% cases. BR was the most common regimen used (52.8%). Overall response rates were seen at 87.8%. Median overall survival, progression free survival and time to next treatment is 8.49 years, 2.15 years and 3.88 years. BR regimen was associated with highest event free survival.

Middle Eastern and North African populations remain underrepresented in genomic databases, comprising less than 1% of genome-wide association study participants despite representing approximately 6% of the global population. Here we present the Egypt Genome Project (EGP1K), in which we performed whole-genome sequencing on 1,024 unrelated Egyptian individuals originating from 21 of Egypts 27 governorates, recruited through eight clinical and research centers across Upper and Lower Egypt. We identified over 51.3 million variants, of which 17.1 million (33.4%) were absent from dbSNP. Allele frequency comparisons across 6.5 million shared variants showed the strongest concordance with Middle Eastern populations ({tau} = 0.977). Principal component analysis and ADMIXTURE modeling at K = 7 revealed that Egyptians share a dominant ancestry component (71.8%) with Middle Eastern populations and carry a smaller Egyptian-enriched component (18.5%) that distinguishes them from neighboring groups. Runs of homozygosity varied substantially across subregions, with Upper Egypt showing the highest burden, paralleling elevated consanguinity rates. Carrier frequency analysis identified MEFV (Familial Mediterranean Fever) at 9.1% as the most prevalent pathogenic carrier state; when adjusted for the national consanguinity rate, MEFV carrier status alone projects approximately 6,600 affected births per year. HLA class I typing identified allele frequencies placing Egyptians within the Levantine-Eastern Mediterranean cluster, providing baseline immunogenetic data currently absent from international databases. Analysis of polygenic risk score distributions revealed substantial differences in threshold-based risk stratification between Egyptians and European reference populations. When the Europeanderived 90th percentile threshold was applied, 83.3% of Egyptians were assigned to high-risk strata for stroke, 76.4% for chronic kidney disease, and 72.8% for gout, compared to the intended 10% high-risk proportion. These distributional shifts were observed across several cardiometabolic traits (Cohens d = 1.55-1.61), while other traits showed closer cross-population concordance, indicating that the degree of threshold miscalibration varies by trait. Together, these findings establish EGP1K as a genomic reference for Egypt and indicate that European-derived risk stratification thresholds may not be directly transferable to the Egyptian population, supporting the need for population-specific calibration of polygenic risk scores.

BackgroundKidney cell lines are widely used to model kidney physiology and disease; however, their gene expression profiles may differ from primary cells due to immortalization, culture conditions, or experimental treatments. Determining whether a cell line resembles its native cell type is critical for interpreting in vitro findings. We developed a transcriptome-based approach that matches bulk RNA-seq data from kidney cell lines, primary cells, or tissues to reference cell types derived from single-cell RNA-seq (scRNA-seq) datasets. MethodsReference transcriptomic profiles were generated from two human and two murine kidney scRNA-seq datasets by pseudobulk aggregation. Bulk RNA-seq data from microdissected kidney tissue, non-kidney negative controls, and kidney cell lines were matched to these references using three statistical similarity measures (Spearman correlation, Euclidean distance, Poisson distance) and three machine learning classifiers (Random Forest, XGBoost, TabPFN). Each was assessed with global gene expression, curated kidney marker gene lists, and the most variable genes. Matching accuracy was evaluated through a three-step validation strategy: within-dataset matching, cross-reference comparison, and validation against primary kidney tissue and negative controls. ResultsGene expression rank-based Spearman correlation and TabPFN, a foundation model for tabular data, emerged as the most accurate and specific approaches, particularly with curated kidney marker gene lists. Both methods correctly identified microdissected kidney tubule segments and were robust against non-kidney negative controls. Applied to commonly used kidney cell lines, OK cells retained proximal tubule identity, particularly under shear stress, while other proximal tubule lines (HK-2, HKC-8, HKC-11) showed inconsistent matching. Collecting duct-derived mIMCD-3 maintained stable similarity across passages, culture conditions, and genetic modifications. ConclusionWe provide two complementary implementations: CellMatchR, an accessible web-based tool using Spearman correlation for routine use, and comprehensive scripts for TabPFN-based matching (link will be added after peer reviewed publication). Together, these resources enable researchers to make informed decisions about kidney cell culture model selection, interpretation, and stability. Translational StatementKidney cell lines are fundamental tools in nephrology research, yet their transcriptomic similarity to native cell types is rarely validated systematically. We demonstrate that combining bulk RNA-seq data with single-cell reference datasets enables robust assessment of cell line identity using gene expression-rank-based correlation and machine learning approaches. By providing a comprehensive evaluation of matching methods, curated kidney marker gene lists, and reference datasets, our study serves as both a practical resource and a methodological framework for the kidney research community, facilitating informed selection of cell culture models, quality control of experimental conditions, developing new experimental cell culture models, and more reliable translation of in vitro findings to kidney physiology and disease.

Diffuse Large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma in the Western world. First-line immunochemotherapy fails in approximately 30-40% of patients, with refractory and relapse patients presenting a dismal prognosis. Currently, these high-risk patients cannot be accurately identified at diagnosis. Using statistical modeling and machine learning approaches applied to large public DLBCL datasets, we identified a novel predictive signature based on the reactivation of eight normally silent tissue-dependent genes associated with survival. We then developed a multiplex RT-MLPseq based assay, compatible with formalin-fixed paraffin-embedded (FFPE) samples and transferable into routine clinical practice, enabling analysis of expression of these eight genes and validated their prognosis impact in an independent real-life cohort. This signature could be integrated with current prognostic indices and molecular classifications to improve patient stratification and guide treatment selection toward a personalized theragnostic approach, thereby enhancing management of non-responder patients.

Introduction: Solitary plasmacytomas (SP) are rare neoplasm of localised proliferation of clonal plasma cells. It can be classified based on site of involvement and bone marrow involvement. It is an indolent disease in the majority of patients. Primary modality of treatment is radiotherapy and surgical excision. Materials and methods: This was a retrospective audit of SP who were treated and followed up at a tertiary care center in eastern India from January 2012 to December 2025. Patients who has solitary plasma cytoma with more than 10% plasma cells, POEMS syndrome, have been excluded from analysis. Results: We identified 46 patients of SP. The median age of the studied population was 53 years (23-75 years). Males were more commonly affected than females (M:F=2.2:1). Most common chief complaints were bony pain (67.4%). SBP was seen in 39 (84.8%) cases whereas SEP was seen in 7 (15.2%) cases. Vertebra was the most common site of involvement (61.4%). Median M band concentration 0.24 g/dL (0.1 to 1.95 gm/dL). IgG was the most common isotype accounting for 60.6% cases. Six cases (13%) had minimal bone marrow involvement. The majority of the patients received local radiotherapy (89.1%). With a median follow up of 5.4 years (95% CI: 1.8 - 9.0), median OS was not reached, median PFS was 9.22 years (95% CI: 5.8-12.6), median time to next treatment (TTNT) was 9.86 years (95% CI: 6.8 - 12.9). Conclusion: Solitary plasmacytoma commonly affects young males. Bones are more commonly affected than extramedullary sites. SP has a lower rate of progression and excellent prognosis when treated with local radiotherapy.

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

Effector CD8+ T cells are key cellular drivers of type 1 diabetes (T1D) pathogenesis, yet questions remain regarding the molecular defects leading to altered cytotoxicity, their signature in peripheral tissues, and their receptor specificity. Thus, we analyzed human pancreatic lymph nodes (pLN) using mass cytometry and single cell RNA sequencing (scRNAseq) with combined proteomic and T cell receptor (TCR) profiling. Cytometric analysis revealed an enriched population of T stem-cell memory (TSCM)-like cells (CD8+CD45RA+CD27+CD28+CCR7+CXCR3+ T cells) in T1D pLNs. scRNAseq profiling indicated an elevated inflammatory cytokine gene signature (IFITM3, LTB) along with regulators of terminal differentiation (BCL6, BCL3), coupled with reduced expression of exhaustion-associated genes (DUSP2, NR4A2, TSC22D3) in CD8+ T cells in T1D pLN. Additionally, effector CD8+ T cells expressed features of progenitor exhausted cells (BCL2) in T1D pLN. Immune Response Enrichment Analysis (IREA) indicated IL-15 signaling as a significant driver of these phenotypes. Integrated TCR and transcriptomic analysis revealed a cluster of diverse naive-like CD8+ T cell clones in T1D pLN. When comparing pLN and pancreatic slice cellular isolates, we observed sharing of effector CD8+ T cells, with upregulation of terminal effector signatures detected within the pancreas relative to paired pLN samples. Multiplex imaging revealed differential localization of TCF1 and TOX expressing T cells in the pancreas, with TCF1+TOX+ cells located in closer proximity to the islets and displaying a mixture of activation and exhaustion-associated phenotypes. Thus, we provide multimodal cellular profiles enriched in T1D tissues for consideration in therapeutic targeting.

Primary light-chain amyloidosis (pAL) is caused by plasma cell (PC) clones that secrete misfolded free light chains that deposit. Anti-CD38 antibody daratumumab is the first-line therapy, while ~10-30% of patients exhibit suboptimal responses (very good partial response, VGPR), and baseline predictors and resistance mechanisms remain under investigation. We generated a single-cell bone marrow atlas with B cell receptor and transcriptome sequencing from a cohort of 30 patients with pAL treated with daratumumab-bortezomib-dexamethasone, including 11 paired pre-/post-treatment samples. Among 27 outcome-evaluable patients, 10 demonstrated suboptimal responses before cycle 6 or the start of subsequent therapy. Among patients with t(11;14), compared with good responders, suboptimal responders' amyloidogenic PCs exhibited lower baseline protein-translation and cell-cell-adhesion gene expression programs, but higher endoplasmic reticulum stress programs. With treatment, mitotic programs were upregulated and gave rise to additional pathogenic PC states. Suboptimal responders also demonstrated two PC-centered immune processes that were enhanced relative to baseline: (i) an inflammatory PTGES2/3-PTGER2/4 axis driven by PTGS2-expressing myeloid-derived suppressor cell-like CD38-negative CD14-positive monocytes that expanded with treatment; and (ii) an immunosuppressive non-classical MHC I axis, in which PCs exerted inhibitory interactions (HLA-E-KLRK1, HLA-G-LILRB1, HLA-F-LILRB1). Consistent with these cell-cell interactions, myeloid cells and NK cells showed functional impairment, while T cells were more exhausted; all three cell types exhibited increased interferon-gamma responses in suboptimal versus good responders. This atlas reveals amyloidogenic PCs' resistance to daratumumab and an inflammatory-immunosuppressive niche driven by prostaglandin and non-classical MHC I, underpinning suboptimal responses.

PurposeClinical translation of CAR T cell therapies has accelerated, yet preclinical evidence still often originates from single-center studies lacking sufficient robustness. Preclinical confirmatory multicenter studies have been proposed to improve the translational success, but their feasibility in cellular therapies remains unexplored. MethodsWe performed a confirmatory multicenter study validating C-C-motive-receptor-8 (CCR8) overexpression in CAR T cells--a strategy previously shown to enhance solid tumor infiltration. In vitro experiments covering activation, cytotoxicity, and migration using three CAR constructs were conducted across two centers with harmonized materials, preregistered protocols, randomization, and blinding. ResultsThe data from the two centers confirmed key findings of the exploratory study: CCR8 overexpression in anti-EpCAM and anti-mesothelin CAR T cells leads to enhanced selective migration towards a CCL1-gradient, while not compromising antigen-specific T cell activatory capacity and cytotoxicity in vitro. The study furthermore broadened the applicability of CCR8 overexpression to anti-CEA CAR T cells. ConclusionsThis first-of-its-kind preclinical confirmatory CAR T study demonstrates the feasibility of a multicenter confirmation in cellular therapy, with technical and logistical challenges resolved through transparent communication between all parties involved. Both exploratory and confirmatory studies aim to downselect CAR candidates with the highest clinical success potential, as they compete for limited resources in preclinical research. It is therefore mandatory to clarify the extent of replications required to validate the experimental methodology and identify CAR candidates with most likelihood of success. TRANSLATIONAL RELEVANCEPreclinical evidence for novel CAR T cell therapeutic strategies relies mostly on exploratory single-center studies lacking robustness, with recent findings substantiating their limited predictive value for cellular therapies tested outside hematology. Here, the function of CCR8-armored CARs in vitro was confirmed in a preclinical confirmatory multicenter study, demonstrating the feasibility of such studies in adding value to the transition of preclinical concepts to clinical development. Our first-of-its-kind study may contribute to define new routes for preclinical testing and further raises the general question of what level of preclinical evidence is reasonably achievable in an academic context. It indicates the need for strong collaborative efforts to realize dedicated preclinical infrastructure for clinical translation of reprogrammed immune cellular therapeutics.

Casein Kinase 2 (CK2) is a constitutively active kinase regulating proliferation and immune signaling and is frequently dysregulated in cancer, including acute myeloid leukemia (AML), making it a therapeutic target. CK2 comprises two catalytic subunits, CK2 or CK2, with two regulatory {beta} subunits. The role of CK2, the predominant catalytic subunit and principal mediator of CK2 kinase activity in hematopoietic cells, in steady-state hematopoiesis remains undefined. To define how CK2 shapes hematopoietic cells, we used bone marrow and spleen tissue samples of wild type control and conditional knock out (KO) of CK2 (Csnk2a1) in the hematopoietic compartment of transgenic mice. Using single-cell RNA sequencing, we profiled the transcriptomic changes associated with CK2 loss. Although HSC abundance was comparable between the control and CK2-deficient samples, HSCs experienced the largest transcriptional response to CK2 loss among all cell types. CK2-deficient HSCs displayed transcriptional remodeling for inflammatory and immune-associated programs, interferon signaling, and antigen presentation. Expression of inflammatory genes such as S100a8 and S100a9, changed in opposite directions in bone marrow and spleen HSCs, demonstrating the transcriptional consequences of CK2 loss shaped by tissue context. Using a network-based approach, we identified immune-associated transcription factors Nfkb1, Rfx5, Hes1, and AP-1 family members as regulatory hubs driving these inflammatory transcriptional states in CK2-deficient HSCs. Cell-cell communication profiling revealed multiple gains and losses in ligand-receptor communication between the HSCs and their immune microenvironment in KO. Our findings identify CK2 as a regulator of immune transcriptional programs in HSCs and suggest that disruption of CK2 signaling influences stem cell behavior and immune activation in contexts relevant to hematologic malignancies and CK2-targeted cancer therapies. Statement of significanceThis study reveals that inhibiting the protein CK2 forces blood stem cells into a stressed, immune-primed state. These tissue-specific findings highlight potential side effects for cancer therapies targeting this essential regulatory kinase.

Acute kidney injury (AKI) is a serious and common clinical syndrome that currently has no effective treatment. Emerging evidence links coagulation pathways to kidney injury, particularly through coagulation proteases. Protease-activated receptors (PARs) are a family of G-protein coupled receptors (GPCRs) that are activated by proteolytic cleavage of their N termini, exposing a tethered ligand that initiates receptor signaling. PARs have been shown to play a major role in inflammation, vascular regulation, and tissue injury. PARs play key roles in inflammation, vascular regulation, and tissue injury. Previous work from the Hamm laboratory demonstrated that PAR4 contributes to AKI progression, as PAR4 knockout mice were protected in both unilateral ureteral obstruction and ischemia-reperfusion-based models of kidney disease. In this study, we investigated the potential of a PAR4 antagonist, VU6073819, at mitigating AKI progression in an ischemia-reperfusion injury (IRI) mouse model. PAR4 antagonism not only alleviated kidney injury and inflammatory response, but it significantly improved the survival. These findings identify PAR4 as a promising therapeutic target for AKI.

Introduction: Podocyte injury is central to the pathogenesis of most glomerulonephritides (GN) and causes segmental glomerulosclerotic lesions that predict progression in IgA Nephropathy (IgAN). Recent advances in high-resolution microscopy and AI-assisted image analysis have enabled detailed quantification of podocyte foot process (FP) morphology. However, whether nanoscale podocyte morphometrics can predict disease progression or treatment response in GN has not been investigated. Aim: To evaluate whether nanoscale podocyte morphometric parameters predict clinical characteristics, disease progression, and treatment response in GN, with a focus on IgAN. Method: Podocyte morphometrics were analyzed in kidney biopsies from patients with GN using high-resolution microscopy and the deep learning-based tool Automatic Morphometric Analysis of Podocytes (AMAP). Four morphometric parameters were quantified: slit diaphragm length (SDL), FP area, FP circularity and FP perimeter. These parameters were correlated with clinical characteristics, conventional electron microscopy (EM) findings and longitudinal follow-up data. Results: The study included 37 patients with GN from Danderyd University Hospital (Stockholm, Sweden), with IgAN representing the largest diagnostic subgroup (n = 19). The median follow-up for the cohort was 3.0 years. SDL correlated significantly with urine albumin-to-creatinine ratio (uACR; p = 0.021), whereas conventional EM measurements did not (p = 0.22). Within the IgAN subgroup, lower SDL was associated with a steeper decline in eGFR, higher FP area with increased long-term proteinuria, and higher FP circularity with improvement in uACR during the first year. The association between lower SDL and eGFR decline remained as a trend in IgAN patients not treated with corticosteroids (p = 0.068) but was absent in the treatment group (p = 0.59). Conclusion: In this proof-of-concept study, nanoscale podocyte morphometrics demonstrated greater sensitivity than conventional EM in quantifying podocyte injury and predicting progression in IgAN. These findings suggest that high-resolution morphometrics may improve risk stratification in IgAN but require validation in larger, independent cohorts before clinical implementation.

BackgroundThe success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. MethodsWe developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). ResultsWe validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 3,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multiethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 seconds per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. ConclusionsGRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors.