By far, gene expression microarrays have been the most used high throughput technology in transplantation to date (6
). Despite delayed adoption of the microarray technology in transplantation, the number of studies using high throughput technologies has been steadily increasing, albeit slowly (7
). Since the publication of the first large transplant microarray study in 2003 (microarrays were invented in 1995), there are now over 70 human studies in public domain using high throughput technologies. These studies examined biopsy, blood and urine samples from different conditions including acute rejection (AR), stable graft functions (STA), chronic injury (CI), tolerance (TOL), and drug response (DR) from transplant patients.
Increased use of high throughput technologies has led to significant improvements in our understanding of the complex allograft injury mechanisms. In a landmark study, Sarwal et al., identified a pivotal role of infiltrating B-cells in acute rejection demonstrating a strong association between presence of dense clusters of B-cells and severe acute rejection (8
). Similar pathogenesis-based transcripts (PBT) expression panels have been inferred from mouse experiments and applied to human transplant expression patterns in an effort to develop correlates of histopathological lesions in renal transplant biopsies (9
). Most recently, Pham, Valantine, and colleagues from the IMAGE Study Group showed in a landmark study how blood-based gene-expression profiling could be used to substitute for biopsy based monitoring after heart transplantation (10
Newer modalities have enabled studies to move past RNA into their coded proteins. To evaluate pathogenicity of non-HLA antibodies after transplantation, Sutherland et al. used newer protein microarrays to identify 36 non-HLA targets in multiple renal transplant patients with acute renal transplant rejection. From this list, protein Kinase C-ζ (PKCζ) was then validated to show that it is a marker of severe allograft injury (11
). Additionally, using protein microarrays Angiotensinogen and PRKRIP1 have been identified as biomarkers of chronic kidney injury, and it is hypothesized that autoantibodies are raised against these unusual targets as they are exposed in the process of cellular damage in the kidney (12
Similar to the progress seen in genomics and proteomics, recent advances in small molecule identification technologies (e.g., mass spectrometry, surface enhanced laser desorption/ionization, Liquid Chromatography/Mass Spectrometry, nuclear magnetic resonance) have given rise to the application of peptidomics and metabolomics to transplantation. Urine is a rich biofluid source for biomarker discovery in organ transplantation. Shotgun proteomics can now map the entire urinary proteome (13
), and evaluate its perturbation during different types of graft injury. Smaller fragments of the urinary peptidome, consisting of degraded byproducts of intact proteins by enzymatic cleavage, can also provide insights into the perturbations in chemical balance during kidney injury (14
). Metabolomics has been used for identifying injury caused by ischemia and reperfusion injury (16
) as well as for monitoring drug toxicity (17
). Metabolomics may be more ideally suited for monitoring drug toxicity than other high throughput technologies, as small molecule drugs and drug metabolites can be specifically measured (19
High throughput technologies have also been expanded to study the role of recently discovered biological entities in transplantation, such as microRNAs (miRNAs). Anglicheau et al. recently used microfluidic cards to profile miRNAs in post-transplantation biopsies to demonstrate their altered expression during AR (20
). Furthermore, they also demonstrated that the miRNAs over-expressed in AR biopsies are also highly expressed in peripheral blood mononuclear cells (PBMCs), which suggested that the intragraft change in miRNA levels may be explained by infiltrating cells, hence, may be used as potential non-invasive biomarkers.