Photopolymerized microdomains in both lipid leaflets establish diffusive transport pathways across biomimetic membranes.

Controlled transport within a network of aqueous subcompartments provides a foundation for the construction of biologically-inspired materials. These materials are commonly assembled using the droplet interface bilayer (DIB) technique, adhering droplets together into a network of lipid membranes. DIB structures may be functionalized to generate conductive pathways by enhancing the permeability of pre-selected membranes, a strategy inspired by nature. Traditionally these pathways are generated by dissolving pore-forming toxins (PFTs) in the aqueous phase. A downside of this approach when working with larger DIB networks is that transport is enabled in all membranes bordering the droplets containing the PFT, instead of occurring exclusively between selected droplets. To rectify this limitation, photopolymerizable phospholipids (23:2 DiynePC) are incorporated within the aqueous phase of the DIB platform, forming conductive pathways in the lipid membranes post-exposure to UV-C light. Notably these pathways are only formed in the membrane if both adhered droplets contain the photo-responsive lipids. Patterned DIB networks can then be generated by controlling the lipid composition within select droplets which creates conductive routes one droplet thick. We propose that the incorporation of photo-polymerizable phospholipids within the aqueous phase of DIB networks will improve the resolution of the patterned conductive pathways and reduce diffusive loss within the synthetic biological network.

Single-cell Analysis of Debrided Diabetic Foot Ulcers Reveals Dysregulated Wound Healing Environment in non-Hispanic Blacks.

Diabetic foot ulcer (DFU) is a critical complication of diabetes, but the wound microenvironment and its healing process are not completely understood. In this study, we optimized single-cell profiling from sharp debrided ulcers. Our findings demonstrate that healing-DFUs were significantly enriched with distinct fibroblasts expressing genes related to inflammation (CHI3L1, IL6) and extracellular matrix remodeling (ASPN), validating our previous studies on surgically resected ulcers. The race-focused analysis depicted lower expression of key healing-associated genes such as CHIL3L1, MMP11, and SFRP4 in fibroblasts of non-Hispanic Black (NHB) patients compared to White patients. In cellular communication analysis, healing enriched fibroblasts of NHBs exhibited upregulation of signaling pathways such as WNT while those of White showed IGF and MK pathways upregulation. Our findings advocate race as a risk marker of DFU outcomes, likely reflecting underlying disparities in environmental exposures and access to care that profoundly influence healing markers. Using sharp debrided tissues for single-cell assays, this study highlights the need for in-depth investigations into dysregulated wound healing microenvironments of under-represented racial groups.

Pediatric T-cell acute lymphoblastic leukemia blast signature and MRD associated immune environment changes defined by single cell transcriptomics analysis.

Different driver mutations and/or chromosomal aberrations and dysregulated signaling interactions between leukemia cells and the immune microenvironment have been implicated in the development of T-cell acute lymphoblastic leukemia (T-ALL). To better understand changes in the bone marrow microenvironment and signaling pathways in pediatric T-ALL, bone marrows collected at diagnosis (Dx) and end of induction therapy (EOI) from 11 patients at a single center were profiled by single cell transcriptomics (10 Dx, 5 paired EOI, 1 relapse). T-ALL blasts were identified by comparison with healthy bone marrow cells. T-ALL blast-associated gene signature included SOX4, STMN1, JUN, HES4, CDK6, ARMH1 among the most significantly overexpressed genes, some of which are associated with poor prognosis in children with T-ALL. Transcriptome profiles of the blast cells exhibited significant inter-patient heterogeneity. Post induction therapy expression profiles of the immune cells revealed significant changes. Residual blast cells in MRD+ EOI samples exhibited significant upregulation (P < 0.01) of PD-1 and RhoGDI signaling pathways. Differences in cellular communication were noted in the presence of residual disease in T cell and hematopoietic stem cell compartments in the bone marrow. Together, these studies generate new insights and expand our understanding of the bone marrow landscape in pediatric T-ALL.

Single-cell RNA sequencing distinctly characterizes the wide heterogeneity in pediatric mixed phenotype acute leukemia.

BACKGROUND: Mixed phenotype acute leukemia (MPAL), a rare subgroup of leukemia characterized by blast cells with myeloid and lymphoid lineage features, is difficult to diagnose and treat. A better characterization of MPAL is essential to understand the subtype heterogeneity and how it compares with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Therefore, we performed single-cell RNA sequencing (scRNAseq) on pediatric MPAL bone marrow (BM) samples to develop a granular map of the MPAL blasts and microenvironment landscape. METHODS: We analyzed over 40,000 cells from nine pediatric MPAL BM samples to generate a single-cell transcriptomic landscape of B/myeloid (B/My) and T/myeloid (T/My) MPAL. Cells were clustered using unsupervised single-cell methods, and malignant blast and immune clusters were annotated. Differential expression analysis was performed to identify B/My and T/My MPAL blast-specific signatures by comparing transcriptome profiles of MPAL with normal BM, AML, and ALL. Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes. RESULTS: B/My and T/My MPAL blasts displayed distinct blast signatures. Transcriptomic analysis revealed that B/My MPAL profile overlaps with B-ALL and AML samples. Similarly, T/My MPAL exhibited overlap with T-ALL and AML samples. Genes overexpressed in both MPAL subtypes' blast cells compared to AML, ALL, and healthy BM included MAP2K2 and CD81. Subtype-specific genes included HBEGF for B/My and PTEN for T/My. These marker sets segregated bulk RNA-seq AML, ALL, and MPAL samples based on expression profiles. Analysis comparing T/My MPAL to ETP, near-ETP, and non-ETP T-ALL, showed that T/My MPAL had greater overlap with ETP-ALL cases. Comparisons among MPAL subtypes between adult and pediatric samples showed analogous transcriptomic landscapes of corresponding subtypes. Transcriptomic differences were observed in the MPAL samples based on response to induction chemotherapy, including selective upregulation of the IL-16 pathway in relapsed samples. CONCLUSIONS: We have for the first time described the single-cell transcriptomic landscape of pediatric MPAL and demonstrated that B/My and T/My MPAL have distinct scRNAseq profiles from each other, AML, and ALL. Differences in transcriptomic profiles were seen based on response to therapy, but larger studies will be needed to validate these findings.

Single-cell analysis reveals altered tumor microenvironments of relapse- and remission-associated pediatric acute myeloid leukemia.

Acute myeloid leukemia (AML) microenvironment exhibits cellular and molecular differences among various subtypes. Here, we utilize single-cell RNA sequencing (scRNA-seq) to analyze pediatric AML bone marrow (BM) samples from diagnosis (Dx), end of induction (EOI), and relapse timepoints. Analysis of Dx, EOI scRNA-seq, and TARGET AML RNA-seq datasets reveals an AML blasts-associated 7-gene signature (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH), which we validate on independent datasets. The analysis reveals distinct clusters of Dx relapse- and continuous complete remission (CCR)-associated AML-blasts with differential expression of genes associated with survival. At Dx, relapse-associated samples have more exhausted T cells while CCR-associated samples have more inflammatory M1 macrophages. Post-therapy EOI residual blasts overexpress fatty acid oxidation, tumor growth, and stemness genes. Also, a post-therapy T-cell cluster associated with relapse samples exhibits downregulation of MHC Class I and T-cell regulatory genes. Altogether, this study deeply characterizes pediatric AML relapse- and CCR-associated samples to provide insights into the BM microenvironment landscape.

Survival Genie, a web platform for survival analysis across pediatric and adult cancers.

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/ .

Single cell transcriptomic landscape of diabetic foot ulcers.

Diabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

Endothelial Reprogramming by Disturbed Flow Revealed by Single-Cell RNA and Chromatin Accessibility Study.

Disturbed flow (d-flow) induces atherosclerosis by regulating gene expression in endothelial cells (ECs). For further mechanistic understanding, we carried out a single-cell RNA sequencing (scRNA-seq) and scATAC-seq study using endothelial-enriched single cells from the left- and right carotid artery exposed to d-flow (LCA) and stable-flow (s-flow in RCA) using the mouse partial carotid ligation (PCL) model. We find eight EC clusters along with immune cells, fibroblasts, and smooth muscle cells. Analyses of marker genes, pathways, and pseudotime reveal that ECs are highly heterogeneous and plastic. D-flow induces a dramatic transition of ECs from atheroprotective phenotypes to pro-inflammatory cells, mesenchymal (EndMT) cells, hematopoietic stem cells, endothelial stem/progenitor cells, and an unexpected immune cell-like (EndICLT) phenotypes. While confirming KLF4/KLF2 as an s-flow-sensitive transcription factor binding site, we also find those sensitive to d-flow (RELA, AP1, STAT1, and TEAD1). D-flow reprograms ECs from atheroprotective to proatherogenic phenotypes, including EndMT and potentially EndICLT.