Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education.

Background: The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods: The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions: The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.

High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology.

This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.

Immunosuppressive CD29+ Treg accumulation in the liver in mice on checkpoint inhibitor therapy.

OBJECTIVE: Liver metastases are often resistant to immune checkpoint inhibitor therapy (ICI) and portend a worse prognosis compared with metastases to other locations. Regulatory T cells (Tregs) are one of several immunosuppressive cells implicated in ICI resistance of liver tumours, but the role played by Tregs residing within the liver surrounding a tumour is unknown. DESIGN: Flow cytometry and single-cell RNA sequencing were used to characterise hepatic Tregs before and after ICI therapy. RESULTS: We found that the murine liver houses a Treg population that, unlike those found in other organs, is both highly proliferative and apoptotic at baseline. On administration of αPD-1, αPD-L1 or αCTLA4, the liver Treg population doubled regardless of the presence of an intrahepatic tumour. Remarkably, this change was not due to the preferential expansion of the subpopulation of Tregs that express PD-1. Instead, a subpopulation of CD29+ (Itgb1, integrin ß1) Tregs, that were highly proliferative at baseline, doubled its size in response to αPD-1. Partial and full depletion of Tregs identified CD29+ Tregs as the prominent niche-filling subpopulation in the liver, and CD29+ Tregs demonstrated enhanced suppression in vitro when derived from the liver but not the spleen. We identified IL2 as a critical modulator of both CD29+ and CD29- hepatic Tregs, but expansion of the liver Treg population with αPD-1 driven by CD29+ Tregs was in part IL2-independent. CONCLUSION: We propose that CD29+ Tregs constitute a unique subpopulation of hepatic Tregs that are primed to respond to ICI agents and mediate resistance.

Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus.

Introduction: Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown. Methods: We analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI). Results: cSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis. Discussion: This study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis.

[This corrects the article DOI: 10.3389/fbinf.2023.1159381.].

S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue.

The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets.

Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin.

Mucosal-associated invariant T (MAIT) cells represent an abundant innate-like T cell subtype in the human liver. MAIT cells are assigned crucial roles in regulating immunity and inflammation, yet their role in liver cancer remains elusive. Here, we present a MAIT cell-centered profiling of hepatocellular carcinoma (HCC) using scRNA-seq, flow cytometry, and co-detection by indexing (CODEX) imaging of paired patient samples. These analyses highlight the heterogeneity and dysfunctionality of MAIT cells in HCC and their defective capacity to infiltrate liver tumors. Machine-learning tools were used to dissect the spatial cellular interaction network within the MAIT cell neighborhood. Co-localization in the adjacent liver and interaction between niche-occupying CSF1R+PD-L1+ tumor-associated macrophages (TAMs) and MAIT cells was identified as a key regulatory element of MAIT cell dysfunction. Perturbation of this cell-cell interaction in ex vivo co-culture studies using patient samples and murine models reinvigorated MAIT cell cytotoxicity. These studies suggest that aPD-1/aPD-L1 therapies target MAIT cells in HCC patients.

A review on deep learning applications in highly multiplexed tissue imaging data analysis.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Early decrease of blood myeloid-derived suppressor cells during checkpoint inhibition is a favorable biomarker in metastatic melanoma.

BACKGROUND: The need for reliable clinical biomarkers to predict which patients with melanoma will benefit from immune checkpoint blockade (ICB) remains unmet. Several different parameters have been considered in the past, including routine differential blood counts, T cell subset distribution patterns and quantification of peripheral myeloid-derived suppressor cells (MDSC), but none has yet achieved sufficient accuracy for clinical utility. METHODS: Here, we investigated potential cellular biomarkers from clinical routine blood counts as well as several myeloid and T cell subsets, using flow cytometry, in two independent cohorts of a total of 141 patients with stage IV M1c melanoma before and during ICB. RESULTS: Elevated baseline frequencies of monocytic MDSCs (M-MDSC) in the blood were confirmed to predict shorter overall survival (OS) (HR 2.086, p=0.030) and progression-free survival (HR 2.425, p=0.001) in the whole patient cohort. However, we identified a subgroup of patients with highly elevated baseline M-MDSC frequencies that fell below a defined cut-off during therapy and found that these patients had a longer OS that was similar to that of patients with low baseline M-MDSC frequencies. Importantly, patients with high M-MDSC frequencies exhibited a skewed baseline distribution of certain other immune cells but these did not influence patient survival, illustrating the paramount utility of MDSC assessment. CONCLUSION: We confirmed that in general, highly elevated frequencies of peripheral M-MDSC are associated with poorer outcomes of ICB in metastatic melanoma. However, one reason for an imperfect correlation between high baseline MDSCs and outcome for individual patients may be the subgroup of patients identified here, with rapidly decreasing M-MDSCs on therapy, in whom the negative effect of high M-MDSC frequencies was lost. These findings might contribute to developing more reliable predictors of late-stage melanoma response to ICB at the individual patient level. A multifactorial model seeking such markers yielded only MDSC behavior and serum lactate dehydrogenase as predictors of treatment outcome.

Neuronal silence as a prosurvival factor for adult-born olfactory bulb interneurons.

Adult-born cells, arriving daily into the rodent olfactory bulb, either integrate into the neural circuitry or get eliminated. However, whether these two populations differ in their morphological or functional properties remains unclear. Using longitudinal in vivo two-photon imaging, we monitored dendritic morphogenesis, odor-evoked responsiveness, ongoing Ca2+ signaling, and survival/death of adult-born juxtaglomerular neurons (abJGNs). We found that the maturation of abJGNs is accompanied by a significant reduction in dendritic complexity, with surviving and subsequently eliminated cells showing similar degrees of dendritic remodeling. Surprisingly, ∼63% of eliminated abJGNs acquired odor responsiveness before death, with amplitudes and time courses of odor-evoked responses similar to those recorded in surviving cells. However, the subsequently eliminated cell population exhibited significantly higher ongoing Ca2+ signals, with a difference visible even 10 days before death. Quantitative supervised machine learning analysis revealed a relationship between the abJGNs' activity and survival probability, with low neuronal activity being supportive for survival.

Positive feedback induces switch between distributive and processive phosphorylation of Hog1.

Cellular decision making often builds on ultrasensitive MAPK pathways. The phosphorylation mechanism of MAP kinase has so far been described as either distributive or processive, with distributive mechanisms generating ultrasensitivity in theoretical analyses. However, the in vivo mechanism of MAP kinase phosphorylation and its activation dynamics remain unclear. Here, we characterize the regulation of the MAP kinase Hog1 in Saccharomyces cerevisiae via topologically different ODE models, parameterized on multimodal activation data. Interestingly, our best fitting model switches between distributive and processive phosphorylation behavior regulated via a positive feedback loop composed of an affinity and a catalytic component targeting the MAP kinase-kinase Pbs2. Indeed, we show that Hog1 directly phosphorylates Pbs2 on serine 248 (S248), that cells expressing a non-phosphorylatable (S248A) or phosphomimetic (S248E) mutant show behavior that is consistent with simulations of disrupted or constitutively active affinity feedback and that Pbs2-S248E shows significantly increased affinity to Hog1 in vitro. Simulations further suggest that this mixed Hog1 activation mechanism is required for full sensitivity to stimuli and to ensure robustness to different perturbations.

Phase-specific signatures of wound fibroblasts and matrix patterns define cancer-associated fibroblast subtypes.

Healing wounds and cancers present remarkable cellular and molecular parallels, but the specific roles of the healing phases are largely unknown. We developed a bioinformatics pipeline to identify genes and pathways that define distinct phases across the time-course of healing. Their comparison to cancer transcriptomes revealed that a resolution phase wound signature is associated with increased severity in skin cancer and enriches for extracellular matrix-related pathways. Comparisons of transcriptomes of early- and late-phase wound fibroblasts vs skin cancer-associated fibroblasts (CAFs) identified an "early wound" CAF subtype, which localizes to the inner tumor stroma and expresses collagen-related genes that are controlled by the RUNX2 transcription factor. A "late wound" CAF subtype localizes to the outer tumor stroma and expresses elastin-related genes. Matrix imaging of primary melanoma tissue microarrays validated these matrix signatures and identified collagen- vs elastin-rich niches within the tumor microenvironment, whose spatial organization predicts survival and recurrence. These results identify wound-regulated genes and matrix patterns with prognostic potential in skin cancer.

Dynamics of Melanoma-Associated Epitope-Specific CD8+ T Cells in the Blood Correlate With Clinical Outcome Under PD-1 Blockade.

Immune checkpoint blockade (ICB) is standard-of-care for patients with metastatic melanoma. It may re-invigorate T cells recognizing tumors, and several tumor antigens have been identified as potential targets. However, little is known about the dynamics of tumor antigen-specific T cells in the circulation, which might provide valuable information on ICB responses in a minimally invasive manner. Here, we investigated individual signatures composed of up to 167 different melanoma-associated epitope (MAE)-specific CD8+ T cells in the blood of stage IV melanoma patients before and during anti-PD-1 treatment, using a peptide-loaded multimer-based high-throughput approach. Additionally, checkpoint receptor expression patterns on T cell subsets and frequencies of myeloid-derived suppressor cells and regulatory T cells were quantified by flow cytometry. Regression analysis using the MAE-specific CD8+ T cell populations was applied to identify those that correlated with overall survival (OS). The abundance of MAE-specific CD8+ T cell populations, as well as their dynamics under therapy, varied between patients. Those with a dominant increase of these T cell populations during PD-1 ICB had a longer OS and progression-free survival than those with decreasing or balanced signatures. Patients with a dominantly increased MAE-specific CD8+ T cell signature also exhibited an increase in TIM-3+ and LAG-3+ T cells. From these results, we created a model predicting improved/reduced OS by combining data on dynamics of the three most informative MAE-specific CD8+ T cell populations. Our results provide insights into the dynamics of circulating MAE-specific CD8+ T cell populations during ICB, and should contribute to a better understanding of biomarkers of response and anti-cancer mechanisms.

High-dimensional immune profiling identifies a biomarker to monitor dimethyl fumarate response in multiple sclerosis.

Dimethyl fumarate (DMF) is an immunomodulatory treatment for multiple sclerosis (MS). Despite its wide clinical use, the mechanisms underlying clinical response are not understood. This study aimed to reveal immune markers of therapeutic response to DMF treatment in MS. For this purpose, we prospectively collected peripheral blood mononuclear cells (PBMCs) from a highly characterized cohort of 44 individuals with MS before and at 12 and 48 wk of DMF treatment. Single cells were profiled using high-dimensional mass cytometry. To capture the heterogeneity of different immune subsets, we adopted a bioinformatic multipanel approach that allowed cell population-cluster assignment of more than 50 different parameters, including lineage and activation markers as well as chemokine receptors and cytokines. Data were further analyzed in a semiunbiased fashion implementing a supervised representation learning approach to capture subtle longitudinal immune changes characteristic for therapy response. With this approach, we identified a population of memory T helper cells expressing high levels of neuroinflammatory cytokines (granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon γ [IFNγ]) as well as CXCR3, whose abundance correlated with treatment response. Using spectral flow cytometry, we confirmed these findings in a second cohort of patients. Serum neurofilament light-chain levels confirmed the correlation of this immune cell signature with axonal damage. The identified cell population is expanded in peripheral blood under natalizumab treatment, substantiating a specific role in treatment response. We propose that depletion of GM-CSF-, IFNγ-, and CXCR3-expressing T helper cells is the main mechanism of action of DMF and allows monitoring of treatment response.

psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data.

MOTIVATION: Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels. RESULTS: We introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes. AVAILABILITY AND IMPLEMENTATION: R package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis. RESULTS: Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree structure. We extract the tree structure by means of a density-based maximum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce density-tree biased autoencoder (DTAE), a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data. AVAILABILITY AND IMPLEMENTATION: Our implementation relying on PyTorch and Higra is available at github.com/hci-unihd/DTAE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Preexisting comorbidities shape the immune response associated with severe COVID-19.

BACKGROUND: Comorbidities are risk factors for development of severe coronavirus disease 2019 (COVID-19). However, the extent to which an underlying comorbidity influences the immune response to severe acute respiratory syndrome coronavirus 2 remains unknown. OBJECTIVE: Our aim was to investigate the complex interrelations of comorbidities, the immune response, and patient outcome in COVID-19. METHODS: We used high-throughput, high-dimensional, single-cell mapping of peripheral blood leukocytes and algorithm-guided analysis. RESULTS: We discovered characteristic immune signatures associated not only with severe COVID-19 but also with the underlying medical condition. Different factors of the metabolic syndrome (obesity, hypertension, and diabetes) affected distinct immune populations, thereby additively increasing the immunodysregulatory effect when present in a single patient. Patients with disorders affecting the lung or heart, together with factors of metabolic syndrome, were clustered together, whereas immune disorder and chronic kidney disease displayed a distinct immune profile in COVID-19. In particular, severe acute respiratory syndrome coronavirus 2-infected patients with preexisting chronic kidney disease were characterized by the highest number of altered immune signatures of both lymphoid and myeloid immune branches. This overall major immune dysregulation could be the underlying mechanism for the estimated odds ratio of 16.3 for development of severe COVID-19 in this burdened cohort. CONCLUSION: The combinatorial systematic analysis of the immune signatures, comorbidities, and outcomes of patients with COVID-19 has provided the mechanistic immunologic underpinnings of comorbidity-driven patient risk and uncovered comorbidity-driven immune signatures.

Privacy-preserving federated neural network learning for disease-associated cell classification.

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell, for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions' input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable with the one obtained with the centralized non-secure solution. PriCell guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data.

Novel Blood Vascular Endothelial Subtype-Specific Markers in Human Skin Unearthed by Single-Cell Transcriptomic Profiling.

Ample evidence pinpoints the phenotypic diversity of blood vessels (BVs) and site-specific functions of their lining endothelial cells (ECs). We harnessed single-cell RNA sequencing (scRNA-seq) to dissect the molecular heterogeneity of blood vascular endothelial cells (BECs) in healthy adult human skin and identified six different subpopulations, signifying arterioles, post-arterial capillaries, pre-venular capillaries, post-capillary venules, venules and collecting venules. Individual BEC subtypes exhibited distinctive transcriptomic landscapes associated with diverse biological pathways. These functionally distinct dermal BV segments were characterized by their unique compositions of conventional and novel markers (e.g., arteriole marker GJA5; arteriole capillary markers ASS1 and S100A4; pre-venular capillary markers SOX17 and PLAUR; venular markers EGR2 and LRG1), many of which have been implicated in vascular remodeling upon inflammatory responses. Immunofluorescence staining of human skin sections and whole-mount skin blocks confirmed the discrete expression of these markers along the blood vascular tree in situ, further corroborating BEC heterogeneity in human skin. Overall, our study molecularly refines individual BV compartments, whilst the identification of novel subtype-specific signatures provides more insights for future studies dissecting the responses of distinct vessel segments under pathological conditions.