Surfing the Big Data Wave: Omics Data Challenges in Transplantation

Applications of transcriptomics in ischemia reperfusion research in lung transplantation

Ischemia-reperfusion (IR) injury contributes to primary graft dysfunction, a major cause of early mortality after lung transplantation. Transcriptomics uses high-throughput techniques to profile the RNA transcripts within a sample and provides a unique view of the mechanisms underlying various biological processes. This review aims to highlight the applications of transcriptomics in lung IR injury studies, which have thus far revealed inflammatory responses to be the major event activated by IR, identified potential biomarkers and therapeutic targets, and investigated the mechanisms of therapeutic interventions. Ex vivo lung perfusion, together with advanced bioinformatic and transcriptomic techniques, including single-cell RNA-sequencing, microRNA profiling, and multi-omics, continue to expand the capabilities of transcriptomics. In the future, the construction of biospecimen banks and the promotion of international collaborations among clinicians and researchers have the potential to advance our understanding of IR injury and improve the management of lung transplant recipients.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

KiT-GENIE, the French genetic biobank of kidney transplantation

KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Modulation of Monocyte Response by Microrna-15b/106a/374a During Antibody-mediated Rejection in Kidney Transplantation

BACKGROUND

Increasing evidence suggest that microRNAs are involved in the physiopathology of acute or chronic renal disease. In kidney transplantation, as key regulators of cellular homeostasis, microRNAs may be involved in the regulation of immune cell function and the allograft response. Here, we investigated the change in circulating microRNA expression profile and their involvement in the profound transcriptional changes associated with antibody-mediated rejection (ABMR).

METHODS

Blood samples were collected at the time of the 710 kidney allograft biopsies at 4 European transplant centers. Messenger RNA and microRNA profiling analyses were performed in a discovery-to-validation study within 3 independent cohorts encompassing N = 126, N = 135, and N = 416 patients, respectively.

RESULTS

Compared with samples with no ABMR, 14 microRNAs were significantly decreased in ABMR samples. Among them, expression levels of microRNA-15b, microRNA-106a, and microRNA-374a gradually decreased with the severity of ABMR lesions. From their in silico-predicted target genes, a high proportion proved to be significantly upregulated in the paired transcriptomic analysis. Gene ontology analyses of microRNA-15b/-106a/-374a suggested enrichment in myeloid-related pathways, which was further refined by in silico and ex vivo transcriptomic analyses, showing a specific origin from classical CD14 + monocytes. Finally, human CD14 + monocytes were subjected to transduction by antago-microRNAs to mimic ABMR pathology. MicroRNA-15b/-106a/-374a impairment resulted in cellular activation with an increased expression of CD69, CRIM1, IPO7, and CAAP1, direct and common targets of the 3 microRNAs.

CONCLUSIONS

Together, our data provide new insights into circulating microRNAs as markers and key players in ABMR, and they suggest monocyte involvement in this process.