RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection.

BACKGROUND: Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management. METHODS: We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways. RESULTS: T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR. CONCLUSIONS: Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.

Transcriptomic signatures of chronic active antibody-mediated rejection deciphered by RNA sequencing of human kidney allografts.

Natural killer (NK) cells mediate spontaneous cell-mediated cytotoxicity and antibody-dependent cell-mediated cytotoxicity. This dual functionality could enable their participation in chronic active antibody-mediated rejection (CA-ABMR). Earlier microarray profiling studies have not subcategorized antibody-mediated rejection into CA-ABMR and active-ABMR, and the gene expression pattern of CA-ABMR has not been compared with that of T cell-mediated rejection (TCMR). To fill these gaps, we RNA sequenced human kidney allograft biopsies categorized as CA-ABMR, active-ABMR, TCMR, or No Rejection (NR). Among the 15,910 genes identified in the biopsies, 60, 114, and 231 genes were uniquely overexpressed in CA-ABMR, TCMR, and active-ABMR, respectively; compared to NR, 50 genes were shared between CA-ABMR and active-ABMR, and 164 genes between CA-ABMR and TCMR. The overexpressed genes were annotated to NK cells and T cells in CA-ABMR and TCMR, and to neutrophils and monocytes in active-ABMR. The NK cell cytotoxicity and allograft rejection pathways were enriched in CA-ABMR. Genes encoding perforin, granzymes, and death receptor were overexpressed in CA-ABMR versus active-ABMR but not compared to TCMR. NK cell cytotoxicity pathway gene set variation analysis score was higher in CA-ABMR compared to active-ABMR but not in TCMR. Principal component analysis of the deconvolved immune cellular transcriptomes separated CA-ABMR and TCMR from active-ABMR and NR. Immunohistochemistry of kidney allograft biopsies validated a higher proportion of CD56+ NK cells in CA-ABMR than in active-ABMR. Thus, CA-ABMR was exemplified by the overexpression of the NK cell cytotoxicity pathway gene set and, surprisingly, molecularly more like TCMR than active-ABMR.

Transmission of yellow fever vaccine virus through blood transfusion and organ transplantation in the USA in 2021: report of an investigation.

BACKGROUND: In 2021, four patients who had received solid organ transplants in the USA developed encephalitis beginning 2-6 weeks after transplantation from a common organ donor. We describe an investigation into the cause of encephalitis in these patients. METHODS: From Nov 7, 2021, to Feb 24, 2022, we conducted a public health investigation involving 15 agencies and medical centres in the USA. We tested various specimens (blood, cerebrospinal fluid, intraocular fluid, serum, and tissues) from the organ donor and recipients by serology, RT-PCR, immunohistochemistry, metagenomic next-generation sequencing, and host gene expression, and conducted a traceback of blood transfusions received by the organ donor. FINDINGS: We identified one read from yellow fever virus in cerebrospinal fluid from the recipient of a kidney using metagenomic next-generation sequencing. Recent infection with yellow fever virus was confirmed in all four organ recipients by identification of yellow fever virus RNA consistent with the 17D vaccine strain in brain tissue from one recipient and seroconversion after transplantation in three recipients. Two patients recovered and two patients had no neurological recovery and died. 3 days before organ procurement, the organ donor received a blood transfusion from a donor who had received a yellow fever vaccine 6 days before blood donation. INTERPRETATION: This investigation substantiates the use of metagenomic next-generation sequencing for the broad-based detection of rare or unexpected pathogens. Health-care workers providing vaccinations should inform patients of the need to defer blood donation for at least 2 weeks after receiving a yellow fever vaccine. Despite mitigation strategies and safety interventions, a low risk of transfusion-transmitted infections remains. FUNDING: US Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority, and the CDC Epidemiology and Laboratory Capacity Cooperative Agreement for Infectious Diseases.

Standardization and Interpretation of RNA-sequencing for Transplantation.

RNA-sequencing (RNA-seq) is a technique to determine the order of nucleotides in an RNA segment. Modern sequencing platforms simultaneously sequence millions of RNA molecules. Advances in bioinformatics have allowed us to collect, store, analyze, and disseminate data from RNA-seq experiments and decipher biological insights from large sequencing datasets. Although bulk RNA-seq has significantly advanced our understanding of tissue-specific gene expression and regulation, recent advances in single-cell RNA-seq have allowed such information to be mapped to individual cells, thus remarkably enhancing our insight into discrete cellular functions within a biospecimen. These different RNA-seq experimental approaches require specialized computational tools. Herein, we will first review the RNA-seq experimental workflow, discuss the common terminologies used in RNA-seq, and suggest approaches for standardization across multiple studies. Next, we will provide an up-to-date appraisal of the applications of bulk RNA-seq and single-cell/nucleus RNA-seq in preclinical and clinical research on kidney transplantation, as well as typical bioinformatic workflows utilized in such analysis. Lastly, we will deliberate on the limitations of this technology in transplantation research and briefly summarize newer technologies that could be combined with RNA-seq to permit more powerful dissections of biological functions. Because each step in RNA-seq workflow has numerous variations and could potentially impact the results, as conscientious citizens of the research community, we must strive to continuously modernize our analytical pipelines and exhaustively report their technical details.

Single nuclei transcriptomics delineates complex immune and kidney cell interactions contributing to kidney allograft fibrosis.

Chronic allograft dysfunction (CAD), characterized histologically by interstitial fibrosis and tubular atrophy, is the major cause of kidney allograft loss. Here, using single nuclei RNA sequencing and transcriptome analysis, we identified the origin, functional heterogeneity, and regulation of fibrosis-forming cells in kidney allografts with CAD. A robust technique was used to isolate individual nuclei from kidney allograft biopsies and successfully profiled 23,980 nuclei from five kidney transplant recipients with CAD and 17,913 nuclei from three patients with normal allograft function. Our analysis revealed two distinct states of fibrosis in CAD; low and high extracellular matrix (ECM) with distinct kidney cell subclusters, immune cell types, and transcriptional profiles. Imaging mass cytometry analysis confirmed increased ECM deposition at the protein level. Proximal tubular cells transitioned to an injured mixed tubular (MT1) phenotype comprised of activated fibroblasts and myofibroblast markers, generated provisional ECM which recruited inflammatory cells, and served as the main driver of fibrosis. MT1 cells in the high ECM state achieved replicative repair evidenced by dedifferentiation and nephrogenic transcriptional signatures. MT1 in the low ECM state showed decreased apoptosis, decreased cycling tubular cells, and severe metabolic dysfunction, limiting the potential for repair. Activated B, T and plasma cells were increased in the high ECM state, while macrophage subtypes were increased in the low ECM state. Intercellular communication between kidney parenchymal cells and donor-derived macrophages, detected several years post-transplantation, played a key role in injury propagation. Thus, our study identified novel molecular targets for interventions aimed to ameliorate or prevent allograft fibrogenesis in kidney transplant recipients.

Selective modulation of gene expression in activated normal human peripheral blood mononuclear cells by store-operated calcium entry blocker BTP2.

Calcium is a critical signaling molecule in many cell types including immune cells. The calcium-release activated calcium channels (CRAC) responsible for store-operated calcium entry (SOCE) in immune cells are gated by STIM family members functioning as sensors of Ca2+ store content in the endoplasmic reticulum. We investigated the effect of SOCE blocker BTP2 on human peripheral blood mononuclear cells (PBMC) stimulated with the mitogen phytohemagglutinin (PHA). We performed RNA sequencing (RNA-seq) to query gene expression at the whole transcriptome level and identified genes differentially expressed between PBMC activated with PHA and PBMC activated with PHA in the presence of BTP2. Among the differentially expressed genes, we prioritized genes encoding immunoregulatory proteins for validation using preamplification enhanced real time quantitative PCR assays. We performed multiparameter flow cytometry and validated by single cell analysis that BTP2 inhibits cell surface expression CD25 at the protein level. BTP2 reduced significantly PHA-induced increase in the abundance of mRNAs encoding proinflammatory proteins. Surprisingly, BTP2 did not reduce significantly PHA-induced increase in the abundance of mRNAs encoding anti-inflammatory proteins. Collectively, the molecular signature elicited by BTP2 in activated normal human PBMC appears to be tipped towards tolerance and away from inflammation.

Urinary cell mRNA profiling of kidney allograft recipients: Development of a portable protocol for noninvasive diagnosis of T cell mediated rejection and BK virus nephropathy.

BACKGROUND: We developed urinary cell mRNA profiling for noninvasive diagnosis of acute T cell mediated rejection (TCMR) and BK virus nephropathy (BKVN), two significant post-transplant complications. Our profiling protocol for the multicenter Clinical Trial of Transplantation-04 (CTOT-04) study consisted of centrifugation of urine to prepare cell pellets, washes, addition of an RNA preservative, storage at 800C and shipment in cold containers to our Gene Expression Monitoring (GEM) Core for RNA isolation and quantification of mRNA in RT-qPCR assays. To simplify profiling, we developed a filter-based protocol (ZFBP) that eliminated the need for centrifugation, RNA preservative, storage at 800C, and shipment in cold containers for mRNA profiling. Furthermore, we trained kidney allograft recipients to perform the filtration of urine at home using the filter and post the urinary cell lysate containing the RNA at ambient temperature to our GEM Core for profiling. Here, we report our refinement of ZFBP and investigation of its diagnostic performance characteristics. METHODS: Total RNA was isolated from kidney allograft biopsy-matched urines using a filter-based protocol complemented by a silica-membrane-based cartridge for mRNA enrichment, the Weill Cornell Hybrid Protocol (WCHP). Absolute copy numbers of CD3ε mRNA, CXCL10 mRNA, and 18S rRNA, components of the CTOT-04 three-gene TCMR diagnostic signature, and urinary cell BKV VP 1 mRNA copy number were measured using RT-qPCR assays. Mann-Whitney test, Fischer exact test, and receiver operating characteristic (ROC) curve analysis were used for data analyses. RESULTS: Urinary cell three-gene TCMR diagnostic signature scores in urines processed using the WCHP discriminated kidney allograft recipients with TCMR (12 TCMR biopsies from 11 patients) from those without TCMR or BKVN (29 No TCMR/No BKVN biopsies from 29 patients). The median (25th and 75th percentiles) score of the CTOT-04 three-gene TCMR diagnostic signature was -0.448 (-1.664, 0.204) in the TCMR group and - 2.542 (-3.267, -1.365) in the No TCMR/ No BKVN group (P = 0.0005, Mann-Whitney test). ROC curve analysis discriminated the TCMR group from the No TCMR/ No BKVN group; the area under the ROC curve (AUROC) was 0.84 (95% Confidence Intervals [CI], 0.69 to 0.98) (P < 0.001), and TCMR was diagnosed with a sensitivity of 67% (95% CI, 35 to 89) at a specificity of 86% (95% CI, 67 to 95) using the CTOT-04 validated cutpoint of -1.213 (P = 0.0016, Fisher exact test). BKV VP1 mRNA copy number in urines processed using the WCHP discriminated patients with BKVN (n = 7) from patients without TCMR or BKVN (n = 29) and the AUROC was 1.0 (95% CI, 1.00 to 1.00) (P < 0.0001) and BKVN was diagnosed with a sensitivity of 86% (95% CI, 42 to 99) at a specificity of 100% (95% CI, 85 to 100) with the previously validated cutpoint of 6.5 × 108 BKV-VP1 mRNA copies per microgram of RNA (P < 0.0001, Fisher exact test). CONCLUSION: Urine processed using the WCHP predicted TCMR and BKVN in kidney allograft recipients. WCHP represents not only a significant advance toward the portability of urinary cell mRNA profiling but also improved patient management by minimizing their visits for urine collection.

Deceased-Donor Acute Kidney Injury and Acute Rejection in Kidney Transplant Recipients: A Multicenter Cohort.

RATIONALE & OBJECTIVE: Donor acute kidney injury (AKI) activates innate immunity, enhances HLA expression in the kidney allograft, and provokes recipient alloimmune responses. We hypothesized that injury and inflammation that manifested in deceased-donor urine biomarkers would be associated with higher rates of biopsy-proven acute rejection (BPAR) and allograft failure after transplantation. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 862 deceased donors for 1,137 kidney recipients at 13 centers. EXPOSURES: We measured concentrations of interleukin 18 (IL-18), kidney injury molecule 1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) in deceased donor urine. We also used the Acute Kidney Injury Network (AKIN) criteria to assess donor clinical AKI. OUTCOMES: The primary outcome was a composite of BPAR and graft failure (not from death). A secondary outcome was the composite of BPAR, graft failure, and/or de novo donor-specific antibody (DSA). Outcomes were ascertained in the first posttransplant year. ANALYTICAL APPROACH: Multivariable Fine-Gray models with death as a competing risk. RESULTS: Mean recipient age was 54 ± 13 (SD) years, and 82% received antithymocyte globulin. We found no significant associations between donor urinary IL-18, KIM-1, and NGAL and the primary outcome (subdistribution hazard ratio [HR] for highest vs lowest tertile of 0.76 [95% CI, 0.45-1.28], 1.20 [95% CI, 0.69-2.07], and 1.14 [95% CI, 0.71-1.84], respectively). In secondary analyses, we detected no significant associations between clinically defined AKI and the primary outcome or between donor biomarkers and the composite outcome of BPAR, graft failure, and/or de novo DSA. LIMITATIONS: BPAR was ascertained through for-cause biopsies, not surveillance biopsies. CONCLUSIONS: In a large cohort of kidney recipients who almost all received induction with thymoglobulin, donor injury biomarkers were associated with neither graft failure and rejection nor a secondary outcome that included de novo DSA. These findings provide some reassurance that centers can successfully manage immunological complications using deceased-donor kidneys with AKI.

Urinary Cell Gene Signature of Acute Rejection in Kidney Allografts.

Introduction: A kidney allograft biopsy may display acute T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), or concurrent TCMR + ABMR (MR). Development of noninvasive biomarkers diagnostic of all three types of acute rejection is a useful addition to the diagnostic armamentarium. Methods: We developed customized RT-qPCR assays and measured urinary cell mRNA copy number in 145 biopsy-matched urine samples from 126 kidney allograft recipients and calculated urinary cell three-gene signature score from log 10 -transformed values for the 18S-normalized CD3E mRNA, 18S-normalized CXCL10 mRNA and 18S rRNA. We determined whether the signature score in biopsy-matched urine specimens discriminates biopsies without rejection (NR, n=50) from biopsies displaying TCMR (n=47), ABMR (n=28) or MR (n=20). Results: Urinary cell three-gene signature discriminated TCMR, ABMR or MR biopsies from NR biopsies (P <0.0001, One-way ANOVA). Dunnett's multiple comparisons test yielded P<0.0001 for NR vs. TCMR; P <0.001 for NR vs. ABMR; and P <0.0001 for NR vs. MR. By bootstrap resampling, optimism-corrected area under the receiver operating characteristic curve (AUC) was 0.749 (bias-corrected 95% confidence interval [CI], 0.638 to 0.840) for NR vs. TCMR (P<0.0001); 0.780 (95% CI, 0.656 to 0.878) for NR vs. ABMR (P<0.0001); and 0.857 (95% CI, 0.727 to 0.947) for NR vs. MR (P<0.0001). All three rejection biopsy categories were distinguished from NR biopsies with similar accuracy (all AUC comparisons P>0.05). Conclusion: Urinary cell three-gene signature score may serve as a universal diagnostic signature of acute rejection due to TCMR, ABMR or MR in human kidney allografts with similar performance characteristics.

Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts.

We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq) analysis of human kidney allograft biopsies will reveal distinct cell types and states and yield insights to decipher the complex heterogeneity of alloimmune injury. We selected 3 biopsies of kidney cortex from 3 individuals for scRNA-seq and processed them fresh using an identical protocol on the 10x Chromium platform; (i) HK: native kidney biopsy from a living donor, (ii) AK1: allograft kidney with transplant glomerulopathy, tubulointerstitial fibrosis, and worsening graft function, and (iii) AK2: allograft kidney after successful treatment of active antibody-mediated rejection. We did not study T-cell-mediated rejections. We generated 7217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches revealed by X and Y chromosome autosomal gene expression, we determined that in AK1 with fibrosis, 42 months after transplantation, more than half of the kidney allograft fibroblasts were recipient-derived and therefore likely migratory and graft infiltrative, whereas in AK2 without fibrosis, 84 months after transplantation, most fibroblasts were donor-organ-derived. Furthermore, AK1 was enriched for tubular progenitor cells overexpressing profibrotic extracellular matrix genes. AK2, eight months after successful treatment of rejection, contained plasmablast cells with high expression of immunoglobulins, endothelial cell elaboration of T cell chemoattractant cytokines, and persistent presence of cytotoxic T cells. In addition to these key findings, our analysis revealed unique cell types and states in the kidney. Altogether, single-cell transcriptomics yielded novel mechanistic insights, which could pave the way for individualizing the care of transplant recipients.

Principles of Virtual Crossmatch Testing for Kidney Transplantation.

Human leukocyte antigens (HLAs) are the primary determinants of alloimmunity. A crossmatch test is a test that determines the immunologic risk of a recipient with a potential donor by ensuring that there are no transplant-relevant circulating antibodies in the recipient directed against donor antigens. Physical crossmatch (PXM) tests, such as complement-dependent cytotoxicity crossmatch (CDCXM) and flow cytometry crossmatch (FCXM), require mixing of patient serum and donor cells, are labor intensive, and are logistically challenging. Virtual crossmatch (VXM) test assesses immunologic compatibility between recipient and potential donor by analyzing the results of 2 independently done physical laboratory tests-patient anti-HLA antibody and donor HLA typing. The goal of VXM is pretransplant risk stratification-though there is no consensus on whether such risk assessment involves predicting the PXM result or the posttransplant outcome. Although the concept of VXM is not new, the advent of solid-phase assays for detecting circulating antibodies in the recipient directed against individual HLA and DNA-based methods for typing donor HLA specificities at a higher resolution makes the routine use of VXM a reality. Accordingly, VXM may be applied at different scenarios-both for sensitized and nonsensitized patients. Implementation of VXM-based approach has resulted in statistically significant reduction in cold ischemia time without an increase in hyperacute rejection episodes. Though there are considerable challenges, VXM is expected to be used more often in the future, depending on the transplant center's tolerance of immunologic risk.

Clinically adjudicated deceased donor acute kidney injury and graft outcomes.

BACKGROUND: Acute kidney injury (AKI) in deceased donors is not associated with graft failure (GF). We hypothesize that hemodynamic AKI (hAKI) comprises the majority of donor AKI and may explain this lack of association. METHODS: In this ancillary analysis of the Deceased Donor Study, 428 donors with available charts were selected to identify those with and without AKI. AKI cases were classified as hAKI, intrinsic (iAKI), or mixed (mAKI) based on majority adjudication by three nephrologists. We evaluated the associations between AKI phenotypes and delayed graft function (DGF), 1-year eGFR and GF. We also evaluated differences in urine biomarkers among AKI phenotypes. RESULTS: Of the 291 (68%) donors with AKI, 106 (36%) were adjudicated as hAKI, 84 (29%) as iAKI and 101 (35%) as mAKI. Of the 856 potential kidneys, 669 were transplanted with 32% developing DGF and 5% experiencing GF. Median 1-year eGFR was 53 (IQR: 41-70) ml/min/1.73m2. Compared to non-AKI, donors with iAKI had higher odds DGF [aOR (95%CI); 4.83 (2.29, 10.22)] and had lower 1-year eGFR [adjusted B coefficient (95% CI): -11 (-19, -3) mL/min/1.73 m2]. hAKI and mAKI were not associated with DGF or 1-year eGFR. Rates of GF were not different among AKI phenotypes and non-AKI. Urine biomarkers such as NGAL, LFABP, MCP-1, YKL-40, cystatin-C and albumin were higher in iAKI. CONCLUSION: iAKI was associated with higher DGF and lower 1-year eGFR but not with GF. Clinically phenotyped donor AKI is biologically different based on biomarkers and may help inform decisions regarding organ utilization.

Conditional deletion of myeloid-specific mitofusin 2 but not mitofusin 1 promotes kidney fibrosis.

Macrophages exert critical functions during kidney injury, inflammation, and tissue repair or fibrosis. Mitochondrial structural and functional aberrations due to an imbalance in mitochondrial fusion/fission processes are implicated in the pathogenesis of chronic kidney disease. Therefore, we investigated macrophage-specific functions of mitochondrial fusion proteins, mitofusin (MFN)1 and MFN2, in modulating macrophage mitochondrial dynamics, biogenesis, oxidative stress, polarization, and fibrotic response. MFN1 and MFN2 were found to be suppressed in mice after adenine diet-induced chronic kidney disease, in transforming growth factor-beta 1-treated bone marrow-derived macrophages, and in THP-1-derived human macrophages (a human leukemic cell line). However, abrogating Mfn2 but not Mfn1 in myeloid-lineage cells resulted in greater macrophage recruitment into the kidney during fibrosis and the macrophage-derived fibrotic response associated with collagen deposition culminating in worsening kidney function. Myeloid-specific Mfn1 /Mfn2 double knockout mice also showed increased adenine-induced fibrosis. Mfn2-deficient bone marrow-derived macrophages displayed enhanced polarization towards the profibrotic/M2 phenotype and impaired mitochondrial biogenesis. Macrophages in the kidney of Mfn2-deficient and double knockout but not Mfn1-deficient mice exhibited greater mitochondrial mass, size, oxidative stress and lower mitophagy under fibrotic conditions than the macrophages in the kidney of wild-type mice. Thus, downregulation of MFN2 but not MFN1 lead to macrophage polarization towards a profibrotic phenotype to promote kidney fibrosis through a mechanism involving suppression of macrophage mitophagy and dysfunctional mitochondrial dynamics.

T-Cell-Positive, B-Cell-Negative Flow Cytometry Crossmatch: Frequency, HLA Locus Specificity, and Mechanisms Among 3073 Clinical Flow Cytometry Crossmatch Tests.

OBJECTIVES: A T-cell-positive and B-cell-negative flow cytometry crossmatch result remains a conundrum since HLA class I antigens are expressed on both T and B cells. We investigated the frequency, donor HLA specificity of the antibodies, and mechanisms for these crossmatch results. MATERIALS AND METHODS: We analyzed 3073 clinical flow cytometry crossmatch tests performed in an American Society of Histocompatibility and Immunogeneticsaccredited histocompatibility laboratory. The sera associated with the T-cell positive and B-cell negative flow cytometry crossmatches were also tested for donor HLA immunoglobulin G antibodies using LABScreen single antigen assays. RESULTS: Among the 3073 test results, 1963 were T-cell negative and B-cell negative, 811 were T-cell negative and B-cell positive, 274 were T-cell positive and B-cell positive, and 25 were T-cell positive and B-cell negative. The LABScreen single antigen assay detected HLA class I immunoglobulin G donor-specific antibodies in 23 of 25 sera associated with a T-cell positive and B-cell negative flow cytometry crossmatch result, and donorspecific antibodies directed at not only HLA-Cw but also at HLA-A or HLA-B were observed. In addition, we identified that the B-cell channel shift threshold used to classify a B-cell flow cytometry crossmatch was a potential contributor to a T-cell-positive and B-cellnegative flow cytometry crossmatch result. CONCLUSIONS: Our analysis of 3073 flow cytometry crossmatches, in addition to demonstrating that HLA antibodies directed at the HLA-A, -B, or -Cw locus were associated with a T-cell-positive and B-cell-negative result, identified mechanisms for the surprising T-cell-positive and B-cell-negative flow cytometry crossmatch result.