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Open Access Basic Medicine Issue
Development of an analytical system for renal spatial immune microenvironment using imaging mass cytometry
Journal of Army Medical University 2026, 48(7): 831-846
Published: 15 April 2026
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Objective

The progression of chronic kidney disease (CKD) is closely associated with the disruption of renal spatial immune microenvironment. This study aimed to develop an integrated experimental and analytical system for dissecting the immune microenvironment of CKD using imaging mass cytometry (IMC), thereby enabling panoramic analysis of single-cell spatial immune microenvironment in CKD renal tissues.

Methods

① Antigenic markers targeting renal structural units, stromal cells, and immune cells were screened to construct a mass cytometry imaging antibody panel; all metal-conjugated antibodies were validated for specificity by immunohistochemistry. ②Two normal kidney biopsy specimens adjacent to ureteral carcinoma, 2 diabetic nephropathy (DN), and 2 IgA nephropathy (IgAN) renal biopsy specimens were collected, stained with the aforementioned antibodies, and subjected to image data acquisition using the Hyperion imaging system to obtain multiplexed spatial data. ③ An integrated Ilastik and CellProfiler workflow was established for single-cell segmentation pipeline tailored for renal tissues, and cell identity annotation was achieved using a combination of manual gating and Phenograph unsupervised clustering. ④ Spatial proximity relationships between cells were defined, and permutation testing was employed to simulate random distribution models to examine spatial relationships between tissue cells and immune cells. ⑤ k-nearest neighbors (kNN) algorithm was applied for spatial clustering, and cellular neighborhoods (CNs) were used to delineate the spatial functional units within renal tissue, followed by analysis of the immunological characteristics and relative abundance of these units.

Results

① A 37-color antibody panel was constructed and optimized, with all antibodies demonstrating high specificity and low background noise; this panel enabled accurate identification of renal structural cells, stromal cells, and infiltrating immune cells. ② High-dimensional IMC imaging data were successfully obtained from normal and CKD renal tissues using the Hyperion imaging system. ③ With the aid of the single-cell segmentation pipeline, a high-resolution single-cell atlas of human kidney was systematically mapped, comprising 21 structural cell types and 17 immune cell subtypes. Immune cell populations such as CD15+ macrophages, GzmK+ CD4+ effector T cells, and 3 distinct double-positive renal tubular cells were identified, revealing differential infiltration of immune cells in DN and IgAN tissues. ④ A single-cell spatial interaction analysis framework was established, demonstrating a disease-specific spatial interaction network of immune cells in CKD tissues, and identifying myofibroblasts as a critical spatial hub connecting tissue injury and immune cell infiltration. ⑤ Spatial neighborhood analysis delineated 9 functional units of renal tissue, including glomerular, proximal tubular, ascending limb of the loop of Henle, vascular and fibrotic, and immune cell-enriched regions. In diseased tissues, the proportion of normal functional regions was decreased, whereas the proportions of fibrotic and immune-enriched regions were increased significantly. Moreover, immune cell-enriched zones maintained close colocalization with various functional regions, while vascular and fibrotic regions were predominantly adjacent to collecting duct and ascending limb of the loop of Henle.

Conclusion

An IMC methodology tailored for the spatial analysis of renal tissue is successfully developed. This platform enables high-resolution, panoramic visualization of spatial distribution of immune cells in chronic renal tissues, dissecting spatial interactions between immune and structural cells and characteristics of local immune microenvironment. This technology holds promise to advance the development of spatially informed precision diagnostics and targeted therapeutic strategies for renal diseases.

Open Access Commentary Issue
Single cell metabolic phenome and genome via the ramanome technology platform: Precision medicine of infectious diseases at the ultimate precision?
iLABMED 2023, 1(1): 5-14
Published: 09 May 2023
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Due to the limitations of existing approaches, a rapid, sensitive, accurate, comprehensive, and generally applicable strategy to diagnose and treat bacterial and fungal infections remains a major challenge. Here, based on the ramanome technology platform, we propose a culture‐free, one cell resolution, phenome‐genome‐combined strategy called single‐cell identification, viability and vitality tests and source tracking (SCIVVS). For each cell directly extracted from a clinical specimen, the fingerprint region of the D2O‐probed single cell Raman spectrum (SCRS) enables species‐level identification based on a reference SCRS database of pathogen species, whereas the C‐D band accurately quantifies viability, metabolic vitality, phenotypic susceptibility to antimicrobials, and their intercellular heterogeneity. Moreover, to source track a cell, Raman‐activated cell sorting followed by sequencing or cultivation proceeds, producinging an indexed, high coverage genome assembly or a pure culture from precisely one pathogenic cell. Finally, an integrated SCIVVS workflow that features automated profiling and sorting of metabolic and morphological phenomes can complete the entire process in only a few hours. Because it resolves heterogeneity for both the metabolic phenome and genome, targets functions, can be automated, and is orders‐of‐magnitude faster while cost‐effective, SCIVVS is a new technological and data framework to diagnose and treat bacterial and fungal infections in various clinical and disease control settings.

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