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The benefits of antibiotics to both human and animal health are undisputed. However, as microbes have become increasingly resistant to antimicrobials and other drugs, scientists have become interested in new solutions to the growing superbug crisis, including the use of defensive microbes and faecal transplants. In new research, Oxford University scientists have developed a lab-based approach, creating positive co-dependent relationships between hosts and bacteria, termed ‘mutualisms’, quickly. These lab-developed bacterial relationships demonstrate how microbes can work with their hosts to prevent infection.
Omega-3 alleviates behavioral and molecular changes in a mouse model of stress-induced juvenile depression
Introduction: Depression is increasingly diagnosed in adolescence, necessitating specific prevention and treatment methods. However, there is a lack of animal models mimicking juvenile depression. This study explores a novel model using ultrasound (US) stress in juvenile mice. Methods: We employed the US stress model in one-month-old C57/BL6 mice, exposing them to alternating ultrasound frequencies (20–25 kHz and 25–45 kHz) for three weeks. These frequencies correspond to negative and neutral emotional states in rodents and can induce a depressive-like syndrome. Concurrently, mice received either an omega-3 food supplement (FS) containing eicosapentaenoic acid (EPA; 0.55 mg/kg/day) and docosahexaenoic acid (DHA; 0.55 mg/kg/day) or a vehicle. Post-stress, we evaluated anxiety- and depressive-like behaviors, blood corticosterone levels, brain expression of pro-inflammatory cytokines, and conducted metabolome analysis of brain, liver and blood plasma. Results: US-exposed mice treated with vehicle exhibited decreased sucrose preference, a sign of anhedonia, a key feature of depression, increased anxiety-like behavior, elevated corticosterone levels, and enhanced TNF and IL-1β gene expression in the brain. In contrast, US-FS mice did not display these changes. Omega-3 supplementation also reduced anxiety-like behavior in non-stressed mice. Metabolomic analysis revealed US-induced changes in brain energy metabolism, with FS increasing brain sphingomyelin. Liver metabolism was affected by both US and FS, while plasma metabolome changes were exclusive to FS. Brain glucose levels correlated positively with activity in anxiety tests. Conclusion: Chronic omega-3 intake counteracted depressive- and anxiety-like behaviors in a US model of juvenile depression in mice. These effects likely stem from the anti-inflammatory properties of the supplement, suggesting potential therapeutic applications in juvenile depression.
Hospital readmission following acute illness among children 2–23 months old in sub-Saharan Africa and South Asia: a secondary analysis of CHAIN cohort
Background: Children in low and middle-income countries remain vulnerable following hospital-discharge. We estimated the incidence and correlates of hospital readmission among young children admitted to nine hospitals in sub-Saharan Africa and South Asia. Methods: This was a secondary analysis of the CHAIN Network prospective cohort enrolled between 20th November 2016 and 31st January 2019. Children aged 2–23 months were eligible for enrolment, if admitted for an acute illness to one of the study hospitals. Exclusions were requiring immediate resuscitation, inability to tolerate oral feeds in their normal state of health, had suspected terminal illness, suspected chromosomal abnormality, trauma, admission for surgery, or their parent/caregiver was unwilling to participate and attend follow-up visits. Data from children discharged alive from the index admission were analysed for hospital readmission within 180-days from discharge. We examined ratios of readmission to post-discharge mortality rates. Using models with death as the competing event, we evaluated demographic, nutritional, clinical, and socioeconomic associations with readmission. Findings: Of 2874 children (1239 (43%) girls, median (IQR) age 10.8 (6.8–15.6) months), 655 readmission episodes occurred among 506 (18%) children (198 (39%) girls): 391 (14%) with one, and 115 (4%) with multiple readmissions, with a rate of: 41.0 (95% CI 38.0–44.3) readmissions/1000 child-months. Median time to readmission was 42 (IQR 15–93) days. 460/655 (70%) and 195/655 (30%) readmissions occurred at index study hospital and non-study hospitals respectively. One-third (N = 213/655, 33%) of readmissions occurred within 30 days of index discharge. Sites with fewest readmissions had the highest post-discharge mortality. Most readmissions to study hospitals (371/450, 81%) were for the same illness as the index admission. Age, prior hospitalisation, chronic conditions, illness severity, and maternal mental health score, but not sex, nutritional status, or physical access to healthcare, were associated with readmission. Interpretation: Readmissions may be appropriate and necessary to reduce post-discharge mortality in high mortality settings. Social and financial support, training on recognition of serious illness for caregivers, and improving discharge procedures, continuity of care and facilitation of readmission need to be tested in intervention studies. We propose the ratio of readmission to post-discharge mortality rates as a marker of overall post-discharge access and care. Funding: The Bill & Melinda Gates Foundation (OPP1131320).
Figure 4 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Renal cortex, but not the papilla, is permissive to long-term survival of <i>Vhl</i>-null cells. <b>A,</b> Representative tdTomato IHC counterstained with hematoxylin in different renal anatomical regions of Control or KO mice harvested at the early or late time points. Scale bar, 100 μm. Magnification, ×20. <b>B,</b> Proportion of cells that are tdTomato-positive in different regions of the kidney as quantified by tdTomato IHC in kidneys from Control or KO mice harvested at different intervals after recombination. <i>n</i> = 7F, 16M for all regions for KO; <i>n</i> = 9F, 15M; 9F, 15M; 9F, 14M; 8F, 14M for cortex, outer medulla, inner medulla, and papilla, respectively, for Control. Line denotes linear regression. Significance testing performed for slope and intercept of linear regression by <i>t</i> test.</p>
Figure 5 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p><i>Vhl</i>-null cells specifically undergo time-dependent alterations in gene expression. <b>A,</b> Density plot depicting UMAP distribution of tdTomato-positive cells from kidneys of Control and KO mice harvested at the early or late time points. <b>B,</b> Left, UMAP plot depicting tdTomato-positive cells from Control and KO mice harvested at the early and late time points colored by UMAP cluster. Right, proportion of cells from each condition belonging to any cluster. <b>C,</b> Scatter plot depicting frequency of expression in tdTomato-positive cells from KO mice at the late time point against log<sub>2</sub>-fold change (log<sub>2</sub>FC) between cells from KO mice at the late versus early time points for all genes. Orange, significantly regulated genes. Genes explicitly mentioned in the main text are labeled. <b>D,</b> Gene set enrichment plots depicting upregulation of genes regulated early after <i>Vhl</i> inactivation (left) or genes known to be HIF targets (right) in <i>Vhl</i>-null cells at the late versus early time points. NES, normalized enrichment score. <i>P</i> value adjusted by Bonferroni correction for multiple testing. <b>E,</b> UMAP plot depicting “PT like” cells among tdTomato-positive cells from Control and KO mice harvested at the early and late time points. Black, PT-like cells. <b>F,</b> Proportion of cells inferred to be “PT like” within tdTomato-positive (top) or tdTomato-negative (bottom) cells across conditions. Median and interquartile range plotted. Pairwise comparisons tested by one-way ANOVA with Holm–Šídák correction. <b>G,</b> Representative CD45 IHC on kidneys from Control (<i>n</i> = 1F, 4M) and KO (<i>n</i> = 6M) mice harvested at the late time point. Scale bar, 50 μm. Magnification, ×40. <b>A–F</b>, scRNA-seq data shown for <i>n</i> = 3F, 1M mice for Control early and Control late samples; <i>n</i> = 2F, 2M mice for KO early and KO late samples.</p>
Figure 6 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p><i>Vhl</i>-null cells exhibit time-dependent proliferation and association with ccRCC-like gene expression. <b>A,</b> Representative dual IHC for tdTomato (brown) and Ki67 (purple) counterstained with hematoxylin in kidneys of KO mice harvested early after recombination. Scale bar, 25 μm. Magnification, ×40. Black arrow, dual-positive cell; red arrow, tdTomato-negative Ki67-positive cell. <b>B,</b> Proportion of tdTomato-positive (top) or tdTomato-negative (bottom) cells that are positive for Ki67 by dual IHC in kidneys of Control and KO mice harvested early and late after recombination (<i>n</i> = 2F, 6M for Control early; <i>n</i> = 4F, 2M for KO early; <i>n</i> = 4F, 5M for Control late; <i>n</i> = 1F, 6M for KO late). Pairwise comparisons by Kruskal–Wallis test with Dunn correction. <b>C,</b> UMAP plot depicting RTE cells from Control and KO mice at the early and late time points. Orange, cells expressing <i>Mki67</i>. <b>D,</b> Proportion of tdTomato-positive (top) or tdTomato-negative (bottom) RTE cells that express <i>Mki67</i> in different conditions. Pairwise comparisons tested by one-way ANOVA with Holm–Šídák correction. <b>E,</b> Proportion of tdTomato-positive cells of different PT identities that express <i>Mki67</i> in different conditions. Pairwise comparisons tested by two-way ANOVA with Holm–Šídák correction. <b>F,</b> Violin plot overlaid with boxplot depicting expression score for genes upregulated in ccRCC cells known to be HIF targets (left) and not known to be HIF targets (right) in tdTomato-positive cells from Control and KO mice harvested at early or late time points. <b>G,</b> Scatter plot depicting changes in mean expression scores for HIF-target (top) and non-HIF-target (bottom) genes specifically upregulated in ccRCC, in tdTomato-positive cells of different PT identities from different conditions when compared with those from Control mice at the early time point. <b>B, D,</b> and <b>E</b>, Median and interquartile range plotted. Only significant (<i>P</i> < 0.05) comparisons shown. <b>C–G,</b> scRNA-seq data shown for <i>n</i> = 3F, 1M mice for tdTomato-positive and tdTomato-negative Control early and Control late samples; <i>n</i> = 2F, 1M mice for tdTomato-negative KO early samples; <i>n</i> = 2F, 2M mice for tdTomato-positive KO early samples; <i>n</i> = 2F, 2M mice for tdTomato-positive and tdTomato-negative KO late samples.</p>
Supplementary Figure S2 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Biallelic Vhl loss entrains early cell-specific transcriptomic changes in renal tubular cells</p>
Figure 5 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p><i>Vhl</i>-null cells specifically undergo time-dependent alterations in gene expression. <b>A,</b> Density plot depicting UMAP distribution of tdTomato-positive cells from kidneys of Control and KO mice harvested at the early or late time points. <b>B,</b> Left, UMAP plot depicting tdTomato-positive cells from Control and KO mice harvested at the early and late time points colored by UMAP cluster. Right, proportion of cells from each condition belonging to any cluster. <b>C,</b> Scatter plot depicting frequency of expression in tdTomato-positive cells from KO mice at the late time point against log<sub>2</sub>-fold change (log<sub>2</sub>FC) between cells from KO mice at the late versus early time points for all genes. Orange, significantly regulated genes. Genes explicitly mentioned in the main text are labeled. <b>D,</b> Gene set enrichment plots depicting upregulation of genes regulated early after <i>Vhl</i> inactivation (left) or genes known to be HIF targets (right) in <i>Vhl</i>-null cells at the late versus early time points. NES, normalized enrichment score. <i>P</i> value adjusted by Bonferroni correction for multiple testing. <b>E,</b> UMAP plot depicting “PT like” cells among tdTomato-positive cells from Control and KO mice harvested at the early and late time points. Black, PT-like cells. <b>F,</b> Proportion of cells inferred to be “PT like” within tdTomato-positive (top) or tdTomato-negative (bottom) cells across conditions. Median and interquartile range plotted. Pairwise comparisons tested by one-way ANOVA with Holm–Šídák correction. <b>G,</b> Representative CD45 IHC on kidneys from Control (<i>n</i> = 1F, 4M) and KO (<i>n</i> = 6M) mice harvested at the late time point. Scale bar, 50 μm. Magnification, ×40. <b>A–F</b>, scRNA-seq data shown for <i>n</i> = 3F, 1M mice for Control early and Control late samples; <i>n</i> = 2F, 2M mice for KO early and KO late samples.</p>
Data from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<div>Abstract<p>Defining the initial events in oncogenesis and the cellular responses they entrain, even in advance of morphologic abnormality, is a fundamental challenge in understanding cancer initiation. As a paradigm to address this, we longitudinally studied the changes induced by loss of the tumor suppressor gene von Hippel Lindau (<i>VHL</i>), which ultimately drives clear cell renal cell carcinoma. <i>Vhl</i> inactivation was directly coupled to expression of a tdTomato reporter within a single allele, allowing accurate visualization of affected cells in their native context and retrieval from the kidney for single-cell RNA sequencing. This strategy uncovered cell type–specific responses to <i>Vhl</i> inactivation, defined a proximal tubular cell class with oncogenic potential, and revealed longer term adaptive changes in the renal epithelium and the interstitium. Oncogenic cell tagging also revealed markedly heterogeneous cellular effects including time-limited proliferation and elimination of specific cell types. Overall, this study reports an experimental strategy for understanding oncogenic processes in which cells bearing genetic alterations can be generated in their native context, marked, and analyzed over time. The observed effects of loss of <i>Vhl</i> in kidney cells provide insights into VHL tumor suppressor action and development of renal cell carcinoma.</p>Significance:<p>Single-cell analysis of heterogeneous and dynamic responses to <i>Vhl</i> inactivation in the kidney suggests that early events shape the cell type specificity of oncogenesis, providing a focus for mechanistic understanding and therapeutic targeting.</p></div>
Supplementary Figure S3 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Vhl-null cells specifically undergo time-dependent alterations in gene expression</p>
Supplementary Table S1 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>scRNA-seq metrics, cell type markers, and lists of differentially expressed genes</p>
Supplementary Figure S1 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Single-cell RNA sequencing on flow-sorted renal cells</p>
Figure 3 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Biallelic <i>Vhl</i> inactivation entrains early cell-specific transcriptomic changes in RTE cells. <b>A,</b> Density plot depicting UMAP distribution of tdTomato-negative and -positive cells from kidneys of Control and KO mice harvested early after recombination. <b>B,</b> Left, UMAP plot depicting cells from Control and KO mice harvested early after recombination colored by UMAP clusters. Right, proportion of cells from each condition belonging to any cluster. <b>C,</b> Scatter plot depicting frequency of expression in tdTomato-negative (top) or tdTomato-positive (bottom) cells from KO mice against log<sub>2</sub>-fold change (log<sub>2</sub>FC) between cells from KO versus Control mice for all genes at the early time point. Orange, significantly regulated genes. Genes explicitly mentioned in the main text are labeled. <b>D,</b> Scatter plot depicting log<sub>2</sub>-fold change between tdTomato-positive cells from KO versus Control for genes significantly regulated in every renal cell identity. Blue, names of HIF target genes. <b>E,</b> PCA of gene expression changes early after <i>Vhl</i> inactivation in different renal cell identities. <b>A–E,</b> scRNA-seq data are shown for <i>n</i> = 3F, 1M for Control negative; <i>n</i> = 3F, 1M mice for Control positive samples; <i>n</i> = 2F, 1M mice for KO negative samples; <i>n</i> = 2F, 2M mice for KO-positive samples.</p>
Supplementary Figure S1 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Single-cell RNA sequencing on flow-sorted renal cells</p>
Figure 6 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p><i>Vhl</i>-null cells exhibit time-dependent proliferation and association with ccRCC-like gene expression. <b>A,</b> Representative dual IHC for tdTomato (brown) and Ki67 (purple) counterstained with hematoxylin in kidneys of KO mice harvested early after recombination. Scale bar, 25 μm. Magnification, ×40. Black arrow, dual-positive cell; red arrow, tdTomato-negative Ki67-positive cell. <b>B,</b> Proportion of tdTomato-positive (top) or tdTomato-negative (bottom) cells that are positive for Ki67 by dual IHC in kidneys of Control and KO mice harvested early and late after recombination (<i>n</i> = 2F, 6M for Control early; <i>n</i> = 4F, 2M for KO early; <i>n</i> = 4F, 5M for Control late; <i>n</i> = 1F, 6M for KO late). Pairwise comparisons by Kruskal–Wallis test with Dunn correction. <b>C,</b> UMAP plot depicting RTE cells from Control and KO mice at the early and late time points. Orange, cells expressing <i>Mki67</i>. <b>D,</b> Proportion of tdTomato-positive (top) or tdTomato-negative (bottom) RTE cells that express <i>Mki67</i> in different conditions. Pairwise comparisons tested by one-way ANOVA with Holm–Šídák correction. <b>E,</b> Proportion of tdTomato-positive cells of different PT identities that express <i>Mki67</i> in different conditions. Pairwise comparisons tested by two-way ANOVA with Holm–Šídák correction. <b>F,</b> Violin plot overlaid with boxplot depicting expression score for genes upregulated in ccRCC cells known to be HIF targets (left) and not known to be HIF targets (right) in tdTomato-positive cells from Control and KO mice harvested at early or late time points. <b>G,</b> Scatter plot depicting changes in mean expression scores for HIF-target (top) and non-HIF-target (bottom) genes specifically upregulated in ccRCC, in tdTomato-positive cells of different PT identities from different conditions when compared with those from Control mice at the early time point. <b>B, D,</b> and <b>E</b>, Median and interquartile range plotted. Only significant (<i>P</i> < 0.05) comparisons shown. <b>C–G,</b> scRNA-seq data shown for <i>n</i> = 3F, 1M mice for tdTomato-positive and tdTomato-negative Control early and Control late samples; <i>n</i> = 2F, 1M mice for tdTomato-negative KO early samples; <i>n</i> = 2F, 2M mice for tdTomato-positive KO early samples; <i>n</i> = 2F, 2M mice for tdTomato-positive and tdTomato-negative KO late samples.</p>
Figure 4 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Renal cortex, but not the papilla, is permissive to long-term survival of <i>Vhl</i>-null cells. <b>A,</b> Representative tdTomato IHC counterstained with hematoxylin in different renal anatomical regions of Control or KO mice harvested at the early or late time points. Scale bar, 100 μm. Magnification, ×20. <b>B,</b> Proportion of cells that are tdTomato-positive in different regions of the kidney as quantified by tdTomato IHC in kidneys from Control or KO mice harvested at different intervals after recombination. <i>n</i> = 7F, 16M for all regions for KO; <i>n</i> = 9F, 15M; 9F, 15M; 9F, 14M; 8F, 14M for cortex, outer medulla, inner medulla, and papilla, respectively, for Control. Line denotes linear regression. Significance testing performed for slope and intercept of linear regression by <i>t</i> test.</p>
Figure 2 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>scRNA-seq on flow-sorted renal cells. <b>A,</b> UMAP plot of tdTomato-negative (left) or tdTomato-positive cells (right) from kidneys of Control mice harvested at the early time point. Cells are colored by inferred cell type. LoH, loop of Henle; DCT, distal convoluted tubule; CD, collecting duct; PC, principal cell; IC, intercalated; PEC, parietal epithelial cell; VSMC, vascular smooth muscle cell; NK, natural killer cell. <b>B,</b> Proportion of sequenced cells inferred to be of each cell type in tdTomato-positive or tdTomato-negative populations from kidneys of Control mice harvested at the early time point. Median and interquartile range plotted. <b>C,</b> UMAP plot depicting expression of PT Module A (left) and PT Module B (right) genes in PT cells from Control mice. <b>D,</b> Representative <i>in situ</i> RNA hybridization exhibiting spatially distinct expression of <i>Neat1</i> (blue) and <i>Fxyd2</i> (red) mRNA in FFPE kidney cortex from Control mice harvested at the early time point. Scale bar, 10 μm. Magnification, ×40. <b>E,</b> UMAP plot depicting cells from Control mice at the early time point. Cells are colored by assigned PT Class. <b>A–E,</b> scRNA-seq data shown for <i>n</i> = 3 female (3F) and <i>n</i> = 1 male (1M) Control mice.</p>
Figure 2 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>scRNA-seq on flow-sorted renal cells. <b>A,</b> UMAP plot of tdTomato-negative (left) or tdTomato-positive cells (right) from kidneys of Control mice harvested at the early time point. Cells are colored by inferred cell type. LoH, loop of Henle; DCT, distal convoluted tubule; CD, collecting duct; PC, principal cell; IC, intercalated; PEC, parietal epithelial cell; VSMC, vascular smooth muscle cell; NK, natural killer cell. <b>B,</b> Proportion of sequenced cells inferred to be of each cell type in tdTomato-positive or tdTomato-negative populations from kidneys of Control mice harvested at the early time point. Median and interquartile range plotted. <b>C,</b> UMAP plot depicting expression of PT Module A (left) and PT Module B (right) genes in PT cells from Control mice. <b>D,</b> Representative <i>in situ</i> RNA hybridization exhibiting spatially distinct expression of <i>Neat1</i> (blue) and <i>Fxyd2</i> (red) mRNA in FFPE kidney cortex from Control mice harvested at the early time point. Scale bar, 10 μm. Magnification, ×40. <b>E,</b> UMAP plot depicting cells from Control mice at the early time point. Cells are colored by assigned PT Class. <b>A–E,</b> scRNA-seq data shown for <i>n</i> = 3 female (3F) and <i>n</i> = 1 male (1M) Control mice.</p>
Data from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<div>Abstract<p>Defining the initial events in oncogenesis and the cellular responses they entrain, even in advance of morphologic abnormality, is a fundamental challenge in understanding cancer initiation. As a paradigm to address this, we longitudinally studied the changes induced by loss of the tumor suppressor gene von Hippel Lindau (<i>VHL</i>), which ultimately drives clear cell renal cell carcinoma. <i>Vhl</i> inactivation was directly coupled to expression of a tdTomato reporter within a single allele, allowing accurate visualization of affected cells in their native context and retrieval from the kidney for single-cell RNA sequencing. This strategy uncovered cell type–specific responses to <i>Vhl</i> inactivation, defined a proximal tubular cell class with oncogenic potential, and revealed longer term adaptive changes in the renal epithelium and the interstitium. Oncogenic cell tagging also revealed markedly heterogeneous cellular effects including time-limited proliferation and elimination of specific cell types. Overall, this study reports an experimental strategy for understanding oncogenic processes in which cells bearing genetic alterations can be generated in their native context, marked, and analyzed over time. The observed effects of loss of <i>Vhl</i> in kidney cells provide insights into VHL tumor suppressor action and development of renal cell carcinoma.</p>Significance:<p>Single-cell analysis of heterogeneous and dynamic responses to <i>Vhl</i> inactivation in the kidney suggests that early events shape the cell type specificity of oncogenesis, providing a focus for mechanistic understanding and therapeutic targeting.</p></div>
Supplementary Figure S2 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>Biallelic Vhl loss entrains early cell-specific transcriptomic changes in renal tubular cells</p>
Supplementary Table S1 from Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type–Specific and Time-Resolved Responses to <i>Vhl</i> Inactivation in the Kidney
<p>scRNA-seq metrics, cell type markers, and lists of differentially expressed genes</p>