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Rodent models in translational circadian photobiology.
Over the last decades remarkable advances have been made in the understanding of the photobiology of circadian rhythms. The identification of a third photoreceptive system in the mammalian eye, in addition to the rods and cones that mediate vision, has transformed our appreciation of the role of light in regulating physiology and behavior. These photosensitive retinal ganglion cells (pRGCs) express the blue-light sensitive photopigment melanopsin and project to the suprachiasmatic nuclei (SCN)-the master circadian pacemaker-as well as many other brain regions. Much of our understanding of the fundamental mechanisms of the pRGCs, and the processes that they regulate, comes from mouse and other rodent models. Here we describe the contribution of rodent models to circadian photobiology, including both their strengths and limitations. In addition, we discuss how an appreciation of both rodent and human data is important for translational circadian photobiology. Such an approach enables a bi-directional flow of information whereby an understanding of basic mechanisms derived from mice can be integrated with studies from humans. Progress in this field is being driven forward at several levels of analysis, not least by the use of personalized light measurements and photoreceptor specific stimuli in human studies, and by studying the impact of environmental, rather than laboratory, lighting on different rodent models.
Penguins snatch seconds-long microsleeps.
Chinstrap penguins fall asleep thousands of times per day in the wild.
Does the claustrum have a function? Lessons from human lesion and animal studies point to divergent multifunctionality
The claustrum is the most densely interconnected region in the human brain. Despite the accumulating data from clinical and experimental studies, the functional role(s) of the claustrum remain unknown. Here, we systematically review claustrum lesion studies and discuss their functional implications. Claustral lesions are associated with an array of signs and symptoms, including changes in cognitive, perceptual and motor abilities; electrical activity; mental state; and sleep. The wide range of symptoms observed following claustral lesions suggests that the claustrum may either have a number of distinct functions, or a global function that impacts many neural processes. We further discuss the implications of these lesions in the context of recent evidence linking the claustrum to sensory perception, sleep, and salience as well as highlighting an underexplored link between the claustrum and pain. We hypothesize that the claustrum is connected to multiple brain networks, both ancient and advanced, which underly fundamental functions as well as higher cognitive processes. Extensive evidence derived from human lesion studies and animal experiments provides unequivocal evidence for a key function of the claustrum as a multifunctional node in numerous networks.
Methods to estimate body temperature and energy expenditure dynamics in fed and fasted laboratory mice: effects of sleep deprivation and light exposure.
Monitoring body temperature and energy expenditure in freely-moving laboratory mice remains a powerful methodology used widely across a variety of disciplines-including circadian biology, sleep research, metabolic phenotyping, and the study of body temperature regulation. Some of the most pronounced changes in body temperature are observed when small heterothermic species reduce their body temperature during daily torpor. Daily torpor is an energy saving strategy characterized by dramatic reductions in body temperature employed by mice and other species when challenged to meet energetic demands. Typical measurements used to describe daily torpor are the measurement of core body temperature and energy expenditure. These approaches can have drawbacks and developing alternatives for these techniques provides options that can be beneficial both from an animal-welfare and study-complexity perspective. First, this paper presents and assesses a method to estimate core body temperature based on measurements of subcutaneous body temperature, and second, a separate approach to better estimate energy expenditure during daily torpor based on core body temperature. Third, the effects of light exposure during the habitual dark phase and sleep deprivation during the light period on body temperature dynamics were tested preliminary in fed and fasted mice. Together, the here-published approaches and datasets can be used in the future to assess body temperature and metabolism in freely-moving laboratory mice.
An integrated approach identifies the molecular underpinnings of murine anterior visceral endoderm migration.
The anterior visceral endoderm (AVE) differs from the surrounding visceral endoderm (VE) in its migratory behavior and ability to restrict primitive streak formation to the opposite side of the mouse embryo. To characterize the molecular bases for the unique properties of the AVE, we combined single-cell RNA sequencing of the VE prior to and during AVE migration with phosphoproteomics, high-resolution live-imaging, and short-term lineage labeling and intervention. This identified the transient nature of the AVE with attenuation of "anteriorizing" gene expression as cells migrate and the emergence of heterogeneities in transcriptional states relative to the AVE's position. Using cell communication analysis, we identified the requirement of semaphorin signaling for normal AVE migration. Lattice light-sheet microscopy showed that Sema6D mutants have abnormalities in basal projections and migration speed. These findings point to a tight coupling between transcriptional state and position of the AVE and identify molecular controllers of AVE migration.
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>