Get inspired at Wichita Remodeling Expo this weekend KSN-TV
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Get inspired at Wichita Remodeling Expo this weekend - KSN-TV
Get inspired at Wichita Remodeling Expo this weekend KSN-TV
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Get inspired at Wichita Remodeling Expo this weekend - KSN-TV
Rate cuts will help entire housing industry, including 'repair and remodeling': Ariel's Bobrinskoy CNBC
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Rate cuts will help entire housing industry, including 'repair and remodeling': Ariel's Bobrinskoy - CNBC
Wilson Food Lion stores celebrate remodeling with gift card giveaways Restoration NewsMedia
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Wilson Food Lion stores celebrate remodeling with gift card giveaways - Restoration NewsMedia
CCMH breaks ground on construction, remodeling project Denison Bulletin Review
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CCMH breaks ground on construction, remodeling project - Denison Bulletin Review
Right place at the right time: Hillview Health Care Center holds rummage sale ahead of $13M remodel La Crosse Tribune
Transgenic mice
All experimental procedures for this study were performed at the Biomedical Center, LMU Munich, in accordance with German and European Union guidelines and were approved by the government of Upper Bavaria. Primary cultures of mouse astrocyte were obtained from the cortex of R26-M2rtTA and Yy1tm2Yshi (ref. 61) mice of P56 days of age. R26-M2rtTA (no. 006965) and Yy1tm2Yshi (no. 014649) mice were obtained from The Jackson Laboratory. The mice were not selected based on their gender. The mice were fed ab libitum; housed in individually ventilated cage systems in a room with a temperature of 22C2C, 55%10% humidity and a 12-h/12-h light/dark cycle; and maintained under specific pathogen-free conditions.
Astrocytes were isolated4,32 by dissecting three postnatal mice (P56), and both the gray and white matter of the cerebral cortex were isolated, after removing the subventricular zone, striatum and hippocampus. The cortical meninges were also removed. The cortical tissue was mechanically dissociated, and the cell suspension was centrifuged at 300g, 4C, for 5min. The cell pellet was resuspended in astrocyte medium consisting of DMEM/F12 (1:1) with GlutaMAX (Thermo Fisher Scientific), 10% FBS, penicillinstreptomycin (Gibco), glucose (Gibco), 1 B27 serum-free supplement (Gibco), 10ngml1 epidermal growth factor (EGF, Gibco) and 10ngml1 basic fibroblast growth factor (bFGF, Gibco). The resulting cell suspension was plated onto a T-25 flask. The primary astrocyte culture was maintained in an incubator for 7d at 37C and 5% CO2. Thereafter, the cells were passaged using 0.05% trypsin/EDTA (Thermo Fisher Scientific) and plated onto the following poly-d-lysine (PDL) (Sigma-Aldrich) coated surfaces for the following experiments: 50,000 cells per well in a 24-well plate in 500l of media for immunocytochemistry; 200,000 cells per six-well plate for bulk-RNA-seq, bulk-ATAC-seq, 10x multiome and 10x single-cell RNA-seq experiments; and 1,000,000 cells per T-25 flask for ChIP-seq.
The plasmid FUW-TetON was modified to insert Gateway cloning sites. Mouse Ngn2, eGFP and 9S-A Ngn2 (referred to as PmutNgn2, which was a gift from A. Philpott)25 were cloned into the Gateway entry vectors (Thermo Fisher Scientific) and subsequently shuttled into the dox-inducible lentiviral expression vector FUW-TetON by employing Gateway recombination cloning technology (Thermo Fisher Scientific). The lentiviral expression vector was characterized by the presence of a tetracycline response element followed by the mammalian CMV2 promoter, which regulated the expression of the TFs and the eGFP (fluorescent reporter employed to identify transduced cells). The TF sequence was separated from the eGFP sequence by an internal ribosome entry site (IRES).
Vesicular stomatitis virus-glycoprotein (VSV-G)-pseudotyped lentiviral particles were produced by transfecting 293T cell line with the following plasmids: pCMVdR8.91 (expressing gag, pol and rev genes), pVSVG and lentiviral expression plasmid. The lentiviral particles were harvested and concentrated by ultracentrifugation at 125,000g for 2h, and the pellet containing the lentiviral particles was resuspended in 1 PBS (supplemented with 5mM MgCl2). The lentivirus was aliquoted and stored at 80C until use. The lentiviral titer was determined by a functional assay, where primary mouse astrocytes were infected with the lentivirus preparation at various dilutions, and the number of successfully infected cells was determined by immunostaining the transduced cells with an anti-GFP antibody (for TF-encoding lentiviruses) or an anti-RFP antibody (for Cre-expressing lentivirus). The viral titers used in all the experiments were in the range of 1010 to 1012 transducing units per milliliter.
After seeding the desired number of cells in PDL-coated plates, 24h later the cells were transduced with 107 to 109 transducing units per microliter of lentiviral particles. Approximately 20h after transduction, the astrocyte medium was replaced with fresh medium containing DMEM/F12 (1:1), supplemented with penicillinstreptomycin, glucose, 1 B27 and GlutaMAX (differentiation medium), and the cells were maintained in culture in a 9% CO2 incubator for a period, depending upon the experimental design. To induce the expression of the TF and fluorescent protein, dox (2gml1) was added to the differentiation medium, and the dox-containing medium was added freshly for four consecutive days.
Cells were prepared for fluorescence-activated cell sorting (FACS) by washing them once with 1 PBS followed by trypsinization (0.05% trypsin in EDTA) for 5min. The trypsinization reaction was stopped by adding astrocyte medium. The harvested cells were then washed twice with ice-cold PBS and centrifuged at 300g for 3min at 4C. The cells were resuspended in DMEM/F-12 (1:1), and a single-cell suspension was generated using a 40-m cell strainer. FACS was performed by employing a FACSAria Fusion (BD Biosciences) using a 100-m nozzle. The gating strategy was set by using forward, side scatter and untransduced astrocytes as a negative control and eGFP-expressing astrocytes as a positive control. Additionally, for Methly-HiC, astrocytes were stained for DAPI, and only cells in G0 and G1 (single DNA content) were sorted. The cells were sorted into DMEM/F-12 (1:1).
Coverslips containing astrocytes were fixed using 4% paraformaldehyde in 1 PBS for 10min at room temperature. The cells were washed twice with 1 PBS and stored for up to 3weeks at 4C before staining. The coverslips were incubated with blocking solution (3% BSA, 0.5% Triton X-100 in 1 PBS) for 30min. Thereafter, the coverslips were incubated with the primary antibody diluted (for detailed information about antibodies used, see Supplementary Table 4) using blocking solution overnight at 4C. After washing the coverslips three times with 1 PBS, they were incubated with the appropriate secondary antibody (diluted 1:500) for 1h at room temperature. The coverslips were stained with DAPI (diluted 1:1,000 in blocking solution) for 10min at room temperature. Finally, the coverslips were mounted using Aqua-Poly/Mount (Polysciences).
A Zeiss Cell Observer was employed to perform continuous live imaging of astrocyte-to-neuron conversion. The acquisition of images was performed as follows. Phase contrast images and fluorescent images (GFP) were captured every 20min and 4h, respectively, with a 10 phase contrast objective (Zeiss) and an AxioCam HRm camera. Zeiss AxioVision 4.7 software was controlled by a custom-made VBA module (TAT, Timm Schroeder, ETH Zrich)62. The movie processing and analysis was performed in ImageJ (1.53q) (National Institutes of Health).
The acquisition of microscopy images was performed using an AxioM2 epifluorescence microscope (Zeiss) or an LSM 710 laser scanning confocal microscope (Zeiss) and ZEN2 software (version 2.0.0.0, Zeiss). The quantification of iNs was performed by applying the following stringent criteria, which were previously described in Gascon et al.5. iNs had to possess a unipolar or bipolar morphology, with a process being at least three times the length of its soma. Additionally, the iNs had to be III-tubulin positive and GFAP negative. In case of the live-imaging microscopy, the time of conversion was defined as a timepoint (in hours) when a GFP+ cell acquired neuronal morphologythat is, exhibited a unipolar or bipolar morphology where the process was at least three times the length of its soma. Statistical analysis was performed in R (version 4.2.1). In Figs. 1eh,j and 7c and Extended Data Fig. 1e, statistical significance was calculated with linear regression by implementing the function lm in RStudio on log2-transformed reprogramming rate14.
The primary astrocytes, transduced with the GFP, Ngn2 or PmutNgn2 lentivirus, were obtained from the same litter of mice. In case of the primary astrocytes obtained from the Yy1tm2Yshi line for the functional studies (conditional knockouts of the candidate gene, Yy1), the wild-type, heterozygote and homozygote genotypes were obtained from same litter of mice by crossing two heterozygote mice.
No statistical methods were used to pre-determine samples sizes, but our sample sizes relied on previous experience, showing that this sample size gives sufficient statistical power5,6,17,18,19,45,63. No data were excluded from the analyses. For data in Figs. 1eh,j and 7c, the values were log transformed and, hence, assumed to be normally distributed.
All the data analysis for immunocytochemistry (Figs. 1eh and 7c) and live imaging (Fig. 1j) was blinded. The genomic experiments and associated data analysis were not blinded because they did not involve subjective measurements.
For the Methyl-HiC experiment, the cells were stained with DAPI following the intracellular staining protocol with the following modifications19. Upon fixing with 1% formaldehyde and permeabilizing the cells, they were stained with DAPI (1:1,000 dilution in wash buffer containing 1% BSA, 0.1% RNasin plus RNase inhibitor (Promega) in PBS). The cells were washed once with the wash buffer and subsequently resuspended in PBS with 1% BSA and 1% RNasin plus RNase inhibitor, filtered through a 40-m cell strainer and FACS sorted.
Approximately 30,000 events per condition were FACS sorted into DMEM/F-12 (1:1) and centrifuged at 300g for 5min at 4C. Then, the cell pellet was resuspended in TRIzol (Thermo Fisher Scientific) and further processed with an RNA Clean & Concentrator Kit (Zymo Research) to extract the RNA. The quality of the extracted RNA was determined using an Agilent RNA 6000 Pico Kit and an Agilent 2100 Bioanalyzer system. All the samples used for library preparation had an RNA integrity number (RIN) value>8.
Next, 50ng of RNA was used as the input material for library generation, and the protocol was a bulk adapted version of mcSCRB-seq64,65. cDNA was generated from the poly(A)-enriched RNA fraction using oligo-dT primers and a Maxima First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). The unincorporated primers were digested using Exonuclease I (Thermo Fisher Scientific). The resulting cDNA was pre-amplified using Terra polymerase (Takara Bio). The quality of the cDNA was determined using the Agilent 2100 Bioanalyzer system. The RNA-seq library was prepared using a NEBNext Ultra II FS DNA Library Kit for Illumina (New England Biolabs) according to the manufacturers instructions. The quality of the RNA-seq libraries was assessed using the Agilent 2100 Bioanalyzer system.
Bulk ATAC-seq libraries were generated by following the OMNI-ATAC-seq protocol66. Approximately 70,000 events were FACS sorted into tubes containing DMEM/F-12 (1:1) and centrifuged at 300g for 5min at 4C, and the cell pellet was resuspended in ATAC resuspension buffer. The cell viability and cell number were determined using a Countess automated cell counter (Thermo Fisher Scientific). Fifty thousand viable cells were used for the Tn5 transposition reaction. The transposition reaction was performed at 37C for 30min in an Eppendorf thermomixer. The transposed fragments were purified using a DNA Clean & Concentrator-5 Kit (Zymo Research). The purified transposed DNA fragments were amplified using NEBNext Ultra II Q5 Master Mix (New England Biolabs) and cleaned up using the DNA Clean & Concentrator-5 Kit. The quality of the ATAC-seq libraries was assessed using the Agilent 2100 Bioanalyzer system.
The ChIP-seq protocol was adapted from a previously described protocol67. In brief, 4 million astrocytes were fixed using 1% methanol-free formaldehyde (Thermo Fisher Scientific) at room temperature for 10min. The cross-linking reaction was terminated by the addition of 125mM glycine followed by an incubation step at room temperature for 5min. The cells were lysed by suspension in a hypotonic buffer (20mM Tris, pH 7.4; 2mM MgCl2; 5% glycerol; 0.6% NP-40) and incubation on ice for 5min with mild vortexing every 30s, which resulted in the release of the nuclei. The nuclei were resuspended in ChIP lysis buffer (20mM Tris, pH 7.4; 150mM NaCl; 1% sodium deoxycholate; 0.1% SDS; 1mM EDTA, pH 8.0) and sonicated using a Bioruptor Pico sonicator (Diagenode) with the following settings: 30s ON/OFF, 20 cycles. The sonicated chromatin was quality controlled using the Agilent 2100 Bioanalyzer system. The sonicated chromatin used for ChIP-seq ranged from 150bp to 300bp.
The chromatin was pre-cleared using Dynabeads Protein G (Thermo Fisher Scientific). After pre-clearing, 10% of the pre-cleared chromatin was set aside as the input fraction. The chromatin was incubated with 4g of mouse monoclonal anti-FLAG M2 antibody (Sigma-Aldrich) overnight at 4C on a rotating wheel (10r.p.m.). After the ChIP, Dynabeads Protein G (Thermo Fisher Scientific) was added to the ChIP sample and incubated at 4C for 3h on a rotating wheel (10r.p.m.). The ChIP sample was washed five times with LiCl was buffer (50mM Tris, pH 7.4; 1mM EDTA, pH 8; 1% NP-40; 1% sodium deoxycholate; 0.5M LiCl) followed by a single wash with TE buffer (10mM Tris, pH 8; 1mM EDTA, pH 8). All the wash steps were performed for 5min at 4C on a rotating wheel (10r.p.m.). The elution of the proteinDNA complex was performed using the elution buffer (50mM NaHCO3, 1% SDS) under the following condition: constant agitation on a thermomixer (Eppendorf) at 60g for 15min at 65C. The eluted DNA was de-crosslinked by the addition of 5M NaCl (final concentration: 210mM) and incubated overnight (not more than 15h) at 65C.
The de-crosslinked DNA was treated with RNase A (Thermo Fisher Scientific) and incubated in a thermomixer (Eppendorf) at 60g for 90min at 37C, followed by treatment with Proteinase K (Ambion) and incubated in a thermomixer (Eppendorf) at 800r.p.m. for 120min at 55C. The DNA was extracted using UltraPure Phenol:Choloroform:Isoamylalcohol (25:24:1, v/v, Thermo Fisher Scientific) following the manufacturers instructions and precipitated by ethanol precipitation (glycogen, 3M sodium acetate, pH 5.2, 100% ethanol) overnight at 20C. The DNA was resuspended in low TE buffer and quantified Qubit dsDNA HS (Thermo Fisher Scientific). One nanogram of ChIP DNA was used as starting material for library preparation with the MicroPlex Library Preparation Kit v2 (Diagenode). The quality of the ATAC-seq libraries was assessed using the Agilent 2100 Bioanalyzer system.
The Yy1, FLAG, Rad21 and H3K27Ac CUT&RUN assays were performed as previously described with specific modifications63.
In brief, 23.6105 iNs were harvested, washed twice and resuspended in wash buffer (20mM HEPES, pH 7.5; 150mM NaCl; 0.5mM spermidine; 1 Roche cOmplete). Concavalin A beads (BioMag Plus, Polysciences) were activated with bead activation buffer (20mM HEPES, pH 7.9; 10mM KCl; 1mM CaCl2; 1mM MnCl2). Cells were incubated with 10l of activated beads for 10min at room temperature. After incubation, the beads were resuspended in a cold antibody buffer (2mM EDTA in digitonin buffer) containing antibody (5g of Yy1 (D5D9Z) rabbit monoclonal antibody 46395, 2g of FLAG antibody (Sigma-Aldrich, F3165-.2MG), 5g of Rad21 (BIOZOL, GTX106012) and 1g of H3k27Ac (Abcam, 39133)), and the mixture was incubated on a nutator overnight at 4C.
On the next day, the beads were washed twice and resuspended in 0.75l of pAG-Mnase in digitonin buffer (0.1% digitonin, Thermo Fisher Scientific, in wash buffer) and incubated for 10min at room temperature on a rotator. Later, beads were washed twice with cold digitonin buffer and then resuspended in 50l of digitonin buffer containing 1l of 100mM CaCl2. The suspension was incubated for 2h at 4C on a nutator. After the incubation, 33l of STOP buffer (340mM NaCl, 20mM EDTA, 4mM EGTA, 50gml1 RNase A (Thermo Fisher Scientific), 50gml1 glycogen) was added to each reaction, and the mixture was incubated for 30min at 37C.
DNA extraction was performed using UltraPure Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v, Thermo Fisher Scientific) and precipitated with 100% ethanol, 1l of glycogen and 1/10th volume of 3M sodium acetate for 416h at 20C. DNA was then dissolved in 10l of 1mM Tris-HCl, pH 8, and 0.1mM EDTA.
CUT&RUN libraries were prepared with an NEBNext Ultra II DNA Library Prep Kit for Illumina using 630ng of fragmented DNA. The quality of the CUT&RUN libraries was evaluated using the Agilent 2100 Bioanalyzer system.
Single-cell multiome (version 1, 10x Genomics) libraries were generated according to the manufacturers instruction manual. In case of the multiome libraries, we targeted for the recovery of 500 nuclei for the GFP, Ngn2 and PmutNgn2 conditions and 5,000 nuclei for the Astro condition.
A modified Methyl-HiC was performed19 based on previously described protocols17,18. Full details of the experimental steps can be found at https://www.protocols.io/view/methylhic-bif2kbqe/.
Pellets from frozen, fixed and FACS-sorted G0/G1 cells were thawed and then lysed on ice with 0.2% Igepal-CA630 (Sigma-Aldrich). Nuclei were subsequently permeabilized with 0.5% SDS and chromatin digested with DpnII (New England Biolabs) at 37C overnight. DpnII was heat inactivated at 62C, and then sticky ends were filled in with biotin-14-dATP (Life Technologies) before proximity ligation with T4 Ligase (New England Biolabs). Proteinase K (New England Biolabs) and NaCl was used for reverse crosslinking nuclei overnight at 68C, and DNA was afterward purified using ethanol precipitation. A Covaris S220 sonicator was next used to shear the DNA to approximately 550-bp fragments.
End repair was performed on the sonicated DNA with T4 DNA Polymerase (New England Biolabs). Approximately 0.01% of methylation controls were spiked into sample, and the reaction was bisulphite converted using an EZ DNA Methylation-Gold Kit (Zymo Research). Libraries were prepared using an Accel-NGS Methyl-Seq DNA Library kit (Swift Biosciences) according to the manufacturers instructions until the adaptor ligation step. At this point, streptavidin T1 beads (Thermo Fisher Scientific) were used for biotin pulldown of DNA, followed by stringent washes. Final libraries were amplified from the streptavidin beads using EpiMark Hot Start Taq (New England Biolabs) with Methyl-Seq indexing primers (Swift Biosciences), followed by size selection with 0.6 AMPure XP beads (Agencourt).
P19 cells were plated in 10-cm dishes. Cells were transfected using Lipofectamine 3000 with 5g of Control (Pcig2), Neurogenin2 and Neurogenin2 mutated (S-A9 TA1) DNAs and were harvested after 24h by cell scraping using cold PBS followed by centrifugation at 300g for 5min to collect the cell pellets.
The P19 cell pellets were thawed on ice and resuspended in 1 pelleted cell volume of the lysis buffer A (10mM HEPES, pH 7.9; 1.5mM MgCl2; 10mM KCl; 0.1% NP40; 1 protease inhibitor cocktail (Roche, 04 693 116 001); 50mM sodium fluoride; 0.2mM sodium orthovanadate; 0.05mM MG132 (Sigma-Aldrich, M7449); 1mM PMSF). After leaving the resuspended cells for 5min on ice, an equal volume of lysis buffer B (10mM HEPES, pH 7.9; 1.5mM MgCl2; 10mM KCl; 0.1% NP40; 1 protease inhibitor cocktail (Roche); 50mM sodium fluoride; 0.2mM sodium orthovanadate; 0.05mM MG132 (Sigma-Aldrich, M7449); 1mM PMSF) was added to leave another 5min on ice. Cells were lysed by pipetting up and down followed by passing through a 27.5-gauge needle (insulin syringe) for 1012 times on ice. This was followed by centrifugation at 15,000g for 15min, and the supernatant was collected. For in vivo samples, embryonic cortex (dorsal telencephalon) was collected at E12.5 and E14.5 to proceed with protein extraction as above.
IP was performed using 2g of anti-YY1 antibody (mouse anti-YY1; Santa Cruz Biotechnology, sc-7341) and control mouse IgG from in vivo (embryonic cortex) and in vitro (P19 cells) samples. Anti-YY1 antibody was incubated with Protein G Magnetic Dynabeads at 4C for 13h in IP 150 KCl buffer (25mM Tris, pH 7.9; 5mM MgCl2; 10% glycerol; 150mM KCl; 0.1% NP40; 0.3mM DTT; 1 protease inhibitor cocktail (Roche, 04 693 116 001), 50mM sodium fluoride; 0.2mM sodium orthovanadate; 0.05mM MG132 (Sigma-Aldrich, M7449); 1mM PMSF). Then, 0.05% NP40 was added to the protein and centrifuged at 17,530g for 15min. The supernatant was collected and added with 0.1mgml1 ethidium bromide to incubate for 30min, followed by centrifugation at 17,530g for 15min. The supernatant was then pre-cleared with Protein G Dynabeads for 1h by end-over-end rotation at 4C. After pre-clearing, protein was added to the Dynabeads, which were previously incubated with anti-YY1 antibody, followed by overnight rotation at 4C. The supernatant was removed after overnight incubation, followed by four washes using PBS with protease inhibitors (0.3mM DTT; 1 protease inhibitor cocktail (Roche, 04 693 116 001); 50mM sodium fluoride; 0.2mM sodium orthovanadate; 0.05mM MG132 (Sigma-Aldrich, M7449); 1mM PMSF). The proteins bound to the beads were eluted using 2 Laemmli buffer, by heating at 95C for 5min. Proteins were separated from beads using a magnet and proceeded to western blotting to visualize the immunoprecipitated proteins.
The immunoprecipitated proteins were run on 12% SDS-PAGE gels at 70V during stacking and 120V while resolving. The proteins were transferred to PVDF membranes (1620177, Bio-Rad) in transfer buffer (25mM Tris; 192mM glycine; 20% methanol, pH 8.3) at 40V overnight at 4C after the SDS-PAGE. Membranes were blocked in TBST (10mM Tris; 100mM NaCl, pH 7.4; 0.1% Tween 20) with 5% (w/v) skim milk for 1h at room temperature and then incubated with primary antibodies overnight at 4C. Membranes were washed 310min in TBST and then incubated for 1h at room temperature with 1/50,000 dilutions of horseradish peroxidase (HRP)-coupled secondary antibodies (anti-rabbit IgG, 7074S, Cell Signaling Technology). Membranes were washed 310min at room temperature and then processed with ECL Plus Western Blotting Reagent (29018904, GE Healthcare) before developing with X-ray film (1141J52, LabForce) and a Bio-Rad ChemiDoc MP Imaging System. The primary antibodies used were rabbit anti-YY1 (Invitrogen, MA5-32052), rabbit anti-Neurogenin2 (Invitrogen, PA5-78556) and rabbit anti-Ezh2 (Cell Signaling Technology, 5246).
Single-cell multiome reads were aligned to the Mus musculus reference genome (GRCm38, mm10), and the quantification was performed using cellranger-arc-2.0.1. Data were analyzed using Signac (version 1.7.0)68 and ArchR44. The quality control (QC) metrics are reported in Supplementary Table 1.
We eliminated low-information content cells based on the following selection criteria: cells where fewer than 1,000 genes and 1,000 unique molecular identifiers (UMIs) (from the gene expression library) and fewer than 8,000 unique fragments per cell, transcription start site (TSS) enrichment <1 and nucleosome signal <0.2 (from the ATAC library) were detected. To avoid including possible doublets in the further analysis, cells where more than 30,000 genes (from the gene expression library) and more than 125,000 unique fragments, TSS enrichment >20 and nucleosome signal >2 (from the ATAC library) were eliminated. Nucleosome signal and TSS enrichment were calculated using Signac (version 1.7.0)68 and plotted using ggplot2. Fragment lengths were calculated using ArchR44 and plotter using ggplot2. Upon filtering out the low-quality cells from all the conditions, the number of cells from the Astro condition was balanced with the other conditions.
The individual modalities (gene expression and ATAC) were normalized and processed using Signac68 and Seurat (version 4.0)33. In brief, peak calling was performed on pseudobulk aggregate per condition using MACS2. A high-quality union peak set was identified by merging the individual peaks and filtering out peaks, which overlapped with a list of blacklisted regions. The count matrix for the high-quality peak set was generated and incorporated into a Seurat object. It was subjected to TF-IDF normalization followed by SVD as described previously. For the gene expression modality, after log transformation, variance-stabilizing transformation was used to perform feature selection. Principal component analysis was performed using the first 20 dimensions. We then computed a joint neighbor graph that represents both gene expression and chromatin accessibility using FindMultiModalNeighbors. We then applied Louvain clustering to cluster cells (resolution=0.2, n.start=20, n.iter=30, algorithm=1), and the cell clusters were visualized using UMAP (min.dist=0.5, spread=1.5, n.components=2L). Cluster identity was determined based on the top 40 differentially expressed genes (MAST, minimum expression change of 0.25 and expressed by at least 25% of the cells in the cluster)69 as well as known marker genes.
Maturation pseudotime analysis was implemented on the QC-approved cells using Monocle3 (refs. 34,35,36,37). The UMAP coordinates was retained from Seurat and used to build the cds object in Monocle3. Cells in cluster iN_1 were selected as the root cells, and a trajectory graph was constructed using the following parameters: minimal_branch_len=5, maxiter=30. The change in gene expression along the constructed trajectory was calculated by fitting a generalized additive model employing cubic regression splines and REML smoothing. The resulting values were rescaled from 0 to 1.
The calculation of motif accessibility deviation scores using position weight matrices obtained from the JASPAR2000 database and Ngn2 ChIP-seq was performed as described previously19 using the ChromVar implementation in Signac68. TF footprints were calculated using ArchR44 and visualized using ggplot2.
To link putative enhancers with their target genes, we used ArchR with empirical P value estimation and k=50. We distinguish among positively correlated (r>0.35; false discovery rate (FDR)<0.1), negatively correlated (r<0.35; FDR<0.1) and non-correlated pairs (0.35 We reasoned that we can predict direct targets of a TF either by using available ChIP-seq peaks or based on the enrichment of the TF motif in the positively correlated EGPs. First, we identified all EGPs that contained the corresponding ChIP-seq peak or TF motif (either in the distal region or in the promoter region). Thereafter, we calculated the gene linkage score by adding up the r2 from each pair per gene (if the peak/motif was contained in the promoter, we used a value of r=1). To calculate enrichment, we used background ATAC peaks with similar GC content and determined significance using a hypergeometric test. A potential limitation of this method is that the significance of peak/motif enrichment for genes with very few identified pairs cannot be accurately calculated. The experimental conditions were labeled according to the manufacturers instructions with the following CellPlex reagents from the 3 CellPlex Kit set A (10x Genomics, PN:1000261): Yy1 WT (CMO309), Yy1 KO (CMO310), Yy1 WT/Ngn2+ (CMO311) and Yy1 KO/Ngn2+ (CMO312). Approximately 25,000 events per condition were FACS sorted (Yy1 WT; untransduced, Yy1 KO; RFP+, Yy1 WT/Ngn2+; GFP+, Yy1 KO/Ngn2+; RFP+GFP+) into an Eppendorf tube. Approximately 33,000 cells were loaded onto a Chromium Next GEM ChIP G (10x Genomics, PN:2000177) to obtain a targeted cell recovery of 20,000 cells. The gene expression library (PN:3000431, single cell 3 v3) and the cell multiplexing library (PN:3000482) were prepared according to the manufacturers protocol (CG000388, Rev A). The gene expression library and the cell multiplexing library were quality controlled using the Agilent 2100 Bioanalyzer, and the libraries were sequenced according to the manufacturers specifications. Single-cell RNA-seq reads were aligned to the Mus musculus reference genome (GRCm38, mm10), and the sample assignment and quantification were performed using cell ranger multi in cellranger-6.0.0. The QC metrics are reported in Supplementary Table 1. We eliminated low-information content cells based on the following selection criteria: cells where fewer than 1,000 genes and 2,500 UMIs were detected. To exclude dead cells, we filtered out cells containing more than 20% mitochondrial reads. To avoid including doublets in the further analysis, cells containing more than 6,000 genes were excluded. Seurat (version 4.0) was used to analyze the cells that passed the filtering steps. The data were normalized using SCTransform, and principal component analysis was performed using the first 25 dimensions. We applied Louvain clustering (resolution=0.6, n.start=20, n.iter=20), and the data were visualized by UMAP projection (min.dist=0.5, spread=1.5, n.components=2L). Cluster identity was determined based on the top 40 differentially expressed genes (MAST, minimum expression change of 0.25 and expressed by at least 25% of the cells in the cluster)69. The ATAC-seq FASTQ files were demultiplexed using Je (version 1.2)70, and the demultiplexed reads were aligned to the mouse genome (GRCm38, mm10). Post-alignment read filtering, peak calling and irreproducible discovery rate (IDR)-based peak filtering were performed by implementing the ENCODE ATAC-seq pipeline. The sequencing and QC metrics are listed in the form of a supplementary data table. The bigWig coverage track was generated using deepTools (version 3.1.3)71. The plotting of the ATAC-seq signal at genomic features was performed using SeqPlots72. The QC metrics are reported in Supplementary Table 1. The RNA-seq FASTQ files were demultiplexed using Je (version 1.2)70; demultiplexed reads were aligned to the mouse genome (GRCm 38, mm10) using STAR (version 2.7.1a)73; and read counts per gene were obtained by using the quantMode GeneCounts option. Further analysis was performed using DEseq2 (ref. 74) in RStudio. The result table for pairwise comparison between PmutNgn2 versus Ngn2 was used the input to generate the GO term enrichment bubble plot in the R package clusterProfiler75. The QC metrics are reported in Supplementary Table 1. The ChIP-seq FASTQ files were demultiplexed using Je (version 1.2)70; demultiplexed reads were aligned to the mouse genome (GRCm38, mm10); and post-alignment read filtering, peak calling and IDR-based peak filtering were performed by implementing the ENCODE ChIP-seq pipeline. The bigWig coverage track was generated using deepTools (version 3.1.3)71. The plotting of the ChIP-seq signal at genomic features was performed using the R package SeqPlots72. The QC metrics are reported in Supplementary Table 1. CUT&RUN data were uniformly processed using CUT&RUN tools 2.0 (ref. 76). Peaks were called using MACS2, and the bigWig coverage track was generated using deepTools (version 3.1.3)71. The QC metrics are reported in Supplementary Table 1. FASTQ files from the Methyl-HiC were mapped to the mouse genome (GRCm38, mm10) by employing JuiceMe77. Further analysis was performed only with uniquely mapping reads (mapq score>30). After the elimination of polymerase chain reaction (PCR) duplicates, the translation of reads into a pair of fragment ends (fends) was achieved by the association of each read with its downstream fend. MethylDackel was used to assess CpG methylation, which entailed the elimination of the initial six nucleotides in the mergeContext mode. Pooling of reads from individual replicates was performed, and, for a cytosine to be considered for further analysis, it had to be in the CpG context and possess at least 10 total coverage. In case of Hi-C, exclusion of reads was based on the following criteria: mapped to the same restriction fragment and separated by less than 1kb. The QC metrics are reported in Supplementary Table 1. Filtered fend-transformed read pairs were imported into the following genome database: mm10 after conversion into misha tracks. The Shaman package was used for read pair normalization (https://tanaylab.bitbucket.io/shaman/index.html), and the calculation of the Hi-C score was performed by employing k-nearest neighbors (kNN)22. The calculation of contact probabilities as a function of genomic distance was previously described22. The insulation score, which is used to define insulation on the basis of observed contacts, was also previously described 19,22,78, and differential TAD boundaries were identified using insulation score19,22. The calculation of contact matrices dominant eigenvector, which have been binned at 250kb, was previously described and performed using publicly available scripts (https://github.com/dekkerlab/cworld-dekker)79. Compartment strength was determined by plotting the log2 ratio of observed versus expected contacts (intrachromosomal separated by at least 10Mb) between AA, BB or AB domains. A ratio between the sum of observed contacts within the A and B compartments and the sum of intercompartment contacts was calculated to determine the compartment strength19. The calculation of insulation and contact enrichment within TADs was previously described19,22. Two complementary approaches were employed for the calculation of contact enrichment ratio at genomic feature pairs, such as Ngn2 ChIP-seq peaks or EGPs. Aggregated Hi-C maps were used to calculate the log2 ratio of observed versus expected contacts within a specified window size, which was centered on the feature of interest. The average enrichment ratio was also calculated for the following: contact strength in the center of the window versus each of the corners. Furthermore, the extraction of kNN-based Hi-C score for each pair in a 10-kb window centered around it and its representation as a scatter plot or box plots enabled the identification of pair-specific trends. Significance testing was performed by using the Wilcoxon rank-sum test. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Here is the original post:
Direct neuronal reprogramming of mouse astrocytes is associated with multiscale epigenome remodeling and requires ... - Nature.com
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Laser ablation of the dorsal skinfold chamber (DSFC) microcirculation
The general patterns of skin microvascular remodeling were similar in all five mice studied (Supplementary Fig. S1, sFig. 1 and Supplementary Tables S1S6, sTables 16). sFigure 1 provides an extensive depiction of the time course and patterns of microvascular network remodeling observed in five distinct DSFC experiments, denoted as Rows AE, each row representing a separate animal experiment. For the in-depth analysis, we focused on Mouse E as a representative case. For this network, we performed a comprehensive anatomical data analysis and mathematical modeling. In sFig. 1, the locations of laser ablations are denoted by the circles (red, arterial; blue, venous). White crosses signify collateral outward remodeling from previously very small vessels, and blue crosses represent outward or inward remodeling of existing arterial/venous segments. Red and blue brackets indicate arterial and venous ablated segment reopening, respectively, and red and blue cross-brackets denote interruption of perfusion in arterial and venous segments, respectively. One to three ablations (except for mouse B D1+) were performed at select locations in the middle of the microvascular network in the largest visible arteries and veins. Remarkably, all specimens exhibit substantial remodeling at different time points from as early as days 13 (sFig. 1, Row B d1 and mouse C d3) and up to day 20 (mouse E, d20) and later (see below). In sFig. 2, mouse A, proximal venous ablation was bypassed through the development of an existing transverse venule, which underwent outward remodeling to match the initial vein diameter. The distal venous ablation revascularized by day 12, while the main vein initially underwent inward remodeling until day 12 and subsequently returned to its pre-ablation diameter by day 17. Arterial ablations and one venous ablation reopened by day 12 in mouse A. In mouse B, by day 5, venous ablations either led to bypass through outward remodeling of transverse veins (mouse C, d5, upper half) or caused inward remodeling of the main venous branch (mouse C, d5, lower half). Mouse C illustrates venous ablations bypassed by pronounced collateral development, while arterial ablation successfully revascularized. In mouse D, initial arterial and venous ablations reopened as early as day 2, while other ablations targeted the main artery and vein and two of their branches to induce flow changes during the period of observation. Mouse E showcases a combination of all remodeling patterns, albeit with varying time courses. Venous ablations revascularize through collateral growth, and arterial occlusions reopen. The majority of vessels display visible remodeling, and diameter data are further described and modeled in subsequent sections of the study.
The detailed diameter values are reported in sTable 1 for intact pre-ablation vessels and sTables 25 for remodeling time points reported in sFig. 1 for the proximal, medial, and distal regions from the closest ablation.
The primary observed remodeling patterns, which encompass outward/inward remodeling of existing arteries and veins, collateral growth of previously small vascular segments, segment reopening, and permanent segment occlusions, are summarized in sTable 6, with accompanying diameter data provided in sTables 25, and illustrated in sFig. 1. One of the notable findings was the presence of both outward and inward remodeling phenomena in both arterial and venous segments, a dynamic process that persisted throughout the observation period. From the onset, immediately after laser ablation at day 0 there were significant diameter changes as shown in sTables 25,although these changes are difficult to observe in sFig. 1. Starting at day1, there was visible collateral remodeling in most specimens.
Furthermore, sTable 6 also highlights the segment occlusion which was the goal of each initial laser ablation. While certain vessels maintained their occluded state throughout the observation period, a subset of vessels displayed the ability to gradually reopen over time. This observation indicates the dynamic nature of microvascular responses and their potential for adaptive adjustments over extended timeframes.
Due to variations in time course remodeling among specimens, a representative mouse (mouse E in sFig. 1 and sTables 16) was chosen to show the observed remodeling process for the remainder of the study. The typical mouse microcirculation within the DSFC contains a main artery and vein pair (Fig.1A,B, solid green arrowhead, and sFig. 1) and smaller arteryvein pairs (open green arrowheads). There are multiple arcade/collateral vessels that connect arteries to other arteries on separate branches of the arterial tree or veins to veins between venous branches. A few arterial collaterals are indicated by red and venous collaterals by blue arrowheads, respectively in (A). These arcading vessels provide vascular redundancy by allowing redistribution of blood flow. Arteries have significantly smaller diameters than the paired veins and have tighter concentric layers of smooth muscle cells (red and yellow in Fig.1, Pre-ablation 13 and Post-ablation 13).
The laser ablation was performed at three major locations (Fig.1A,B, two artery/vein pairs in regions 1 and 3, and an artery in region 2) in the center of the window to maximize blood flow redistribution and to allow long term observation of the developing vascular changes (as some drifting of the tissue occurs within the DSFC over two weeks). The ablated vessels experienced rapid vasoconstriction upstream and downstream from the ablation site (Fig.1, Post-ablation 13) as observed before25,26. There was complete blood flow interruption in segments just distal and proximal from the ablations (sVideos 1A, 2A and 3A). The laser ablation procedure was focused only on the target vessels, effectively cauterizing them while having little effect on the surrounding tissue as shown before in similar experimental settings26. The brown scar tissue located in the muscle fascia subsides at later time points (sVideos 1B, 2B and 3B). Note that in region 2, the ablation of the artery had no effect on the diameter of the adjacent large vein or the blood flow in that vessel (Fig.1, Post-ablation 2; sVideo 2A).
By day 6 after ablation, there was clear evidence of vascular remodeling throughout the network (compare Fig. 2D0/+and D6). Vessel segments associated with the ablated vessels had reduced diameter at day 6, while there was increased diameter in a number of collateral vessels (regions 4, 5 in Fig.2D6). By day 13, vessel diameters had qualitatively returned to pre-ablation values for much of the network (Fig.2D13). This was due to remodeling of collateral vessels, which allowed an increase in compensatory flow entering tissue regions previously supplied by the ablated vessels. There were also large increases in diameter in a few small vessels that restored flow through the veins by bypassing the ablation sites (arrowheads in Fig.2, D13, and sVideo 1AD, 3AD).
Time course of vascular remodeling post-ablation. D0- and D0+ indicate pre-ablation and post-ablation on Day 0, respectively. Regions 13 indicate the ablation regions and site (yellow line). Shown are images through Day 30 (D30). Initially, at Days 6 vessel redundancy and remodeling in areas compensate for the ablation-induced ischemia. By day 13, the venous connections were reestablished (clear and black arrowheads). From day 20, the artery in Region 1 has reconnected (green arrowhead) to mimic the original path, increasing flow to the downstream network. There was no angiogenic regeneration of the ablated veins; instead, flow quickly re-routed through small pre-existing venules that appeared to be pre-existing connections at either side of the damage site (clear and black arrowheads, D1330). The white scale bars are 1mm.
These structures formed from sequences of smaller microvessels that were part of the original vascular bed. It is likely that increased flow through these small bypass channels caused the expansion of vessel diameter which eventually matched that of the original vein, similar to previous observations in the mouse gracilis muscle2.
Some branches from the two small networks in regions 1 and 3 associated with the new vein segments appeared to be pruned or regressed as the new segments became part of the large veins. Albeit observed at low/medium resolution in transmitted light images, in these veins, there was no visible evidence of extensive angiogenesis or new vessel growth contributing to the regeneration of the network or restoration of flow. Rather, the rerouting occurred through remodeling of existing vessel segments, most of which could be visualized even before the ablations were performed.
However, we did observe reconnection of venous segments through the ablation site via endothelial migration in other networks (sFig. 1, rows AC and E). The response to injury appears to be related to the effective blood pressure difference across the ablation. In Fig.2, regions 1 and 3, the ablations are situated such that there is a large pressure drop across the ablation sites. This forces the blood to reroute through the smaller vessels early after the injury. However, in sFig.1 row A, there were two ablations performed on the same large vein. In this case, the upstream ablation has little pressure drop because the downstream ablation is preventing outflow. For this reason, very little flow re-routing or vessel remodeling occur at the upstream ablation, and this region was instead reperfused by direct reconnection of the vein via angiogenesis (sFig. 1, row A, d12 and d17).
On the arterial side, in region 2 we did not observe re-routing locally through pre-existing microvessels, and their subsequent enlargement, as in the veins of regions 1 and 3, Fig.2. Instead, flow was redistributed through the preexisting arterial arcades to circumvent the ablation and compensate for the lowered flow distal to the ablation sites (Fig.2. D6 and D13, areas 46, and sFig. 1C, d3 and d18). Compared with the venous rerouting in regions 1 and 3 in Fig.2, which occurred over very short distances (~1mm) around the ablations, rerouting on the arterial side extended over much larger distances (~510mm) through the arcade vessels. In the ablated arteries, we did observe reconnection of the vessel through the ablation site via angiogenesis to mimic the original path. On days 20, 23, 28 and 30, there was evidence of regeneration on the arterial side, as the artery ablated in Region 1 (Fig.2) reconnected (for example, see the arterial ablation in region 1 (Fig.2, D630, green arrowheads, and sVideo 1AD). As this new vessel segment grew, original flow through the artery was restored, and the diameters of the major compensating collaterals decreased (Fig.2, D28, region 8). The artery in region 2 (Fig.2, D630, yellow arrowheads) did not achieve reconnection by the 30-day time point although some small flow pathways can be traced (sVideos 2C and 3C). The arterial flow in region 3 was re-established by day 30 but via smaller vessels than the original artery (Fig.2, D30 blue arrowhead), with blood flow evident via Doppler OCT at day 14 (Fig.5, D14b) and intravital BF imaging at later time points (sVideo 3D).
Because of the endogenous reporters expressed by the mice, we were able to visualize endothelial cells (TIE2-GFPgreen) and smooth muscle cells (aSMA-dsRedred) longitudinally at the ablation sites. In vivo laser confocal imaging of regions 2 and 3 in Fig.1 revealed migration of the endothelial and smooth muscle cells through the ablation sites (Fig.3). In region 3, the vascular pathway was re-established, and blood flow was observed (Fig.3D). Both endothelial and smooth muscle cells migrated into the damaged region and appeared to establish a connection by day 30, based on Doppler OCT imaging (see Fig.5). A similar process was observed for the other artery, which was ablated at location 2 in Fig.1 (Fig.3A, B), although this vessel did not reconnect by the end of our observation period. Angiogenesis was not observed in the large vein that remodeled in region 3, but the remodeled region acquired a covering of smooth muscle cells (Fig.3C). After day 30, the relevant vessels had shifted out of the window chamber and were no longer observable.
Vessel regeneration at Day 30. At top is a brightfield image of regions 2 and 3 from Fig.1. Four regions are shown in detail with multiphoton imaging of the endogenous TIE2-GFP (endothelial cells) and aSMA-dsRed (smooth muscle cells). The ablated regions are shown by the circles. In these regions, there was evidence of angiogenesis in the arterial network as endothelial cells (solid arrowheads) and smooth muscle cells (open arrowhead) migrated into the ablated regions. At this time point, the remodeled vein segment in region 3, Fig.1 has matured, with a covering of smooth muscle cells (arrow, C). The scale bar is 1mm.
Overall, both arteries and veins changed their diameters collectively over time (Fig.4 and sFig. 1 and sTables 26). Because of resolution limitations, we restricted the quantitative analysis to the main arteries and veins and their transverse branches with inner diameters larger than 11m; therefore, the histograms do not include smaller vessels and capillaries. The smallest arteries (30m centered bin) stayed almost constant during the time points studied. A small dip at day 6 was recovered and slightly increased at the later time points. Combined with changes at other time points this could mean that smaller vessels became larger and therefore visible in this diameter range. The largest change in diameter distribution was observed in the 60m bin which was increased at days 620 and went back to normal values by day 30 which suggests a transient increase in vessel diameters to accommodate the early changes in blood flow as we noticed before in the gracilis artery remodeling2,4. Some larger vessels also constricted, moving from the 90150m to the 60m range. At day 16, this trend reversed temporarily while between days 2028 a lot of the larger arteries were still constricted. By day 30 diameter distribution of all arteries was close to post-ablation and pre-ablation values even in the absence of the ablated large artery suggesting that blood redistribution can be accomplished through the contribution of the network of smaller arterioles even in the absence of the large artery.
The frequency distribution of vessel diameter for arteries (top) and veins (bottom) pre and up to 30days post-ablation. Post-ablation, the distribution of artery diameters is skewed towards more smaller diameter vessels suggesting the blood is redirected from large arteries to smaller alternative pathways. This trend is reversed towards a more normal distribution (more larger vessels) past day 16. On the venous side, the distribution of diameters is more stable reflecting a larger capacity of the venous side to accommodate blood flow redistribution without major diameter changes in most of the vessels.
The vein diameter distribution is more spread over a larger range of diameters suggesting a larger adaptation of the veins to accommodate flow changes. The largest variation in diameter distribution was observed in the 30m bin although a slight transient tendency is also observed between days 6 and 28 with a decrease to normal values at day 30. During the transient increase period, an interesting second transient decrease was observed at day 16. Veins in the 80m range exhibited a gradual increase starting from post-ablation and peaking at day 30. The veins with diameters in 130180m range showed the largest increase in density at early and medium time points (days 6 and 16). The largest veins stayed open immediately following the ablation, at day 6 they were reduced in diameter, at days 16 and 20 they were close to normal values but by day 30, the number of larger veins was drastically reduced suggesting again that on the venous side like the arterial side, flow redistribution could also be accomplished via a larger network of smaller venules.
We next focused on individual vessels to determine how specific vessels contributed to the flow redistribution. Using quantitative flowmetry OCT methods based on amplitude-decorrelation which can be used to estimate flow rate as well as lumen diameters30,31, we analyzed a number of segments distal and proximal to the ablations sites before and following the ablations (Fig.5). We also used intravital BF microscopy to determine flow directions (see Supplementary Videos S1S3). In the intact network, the blood flows from left to right from the large artery (#2, Fig.5) to its branches (#4, 6 and 9). The blood flows from the venous branches (#3, 5, 7, 8,10 and 11) towards the main vein (#1). Following ablation, the blood flow stopped in the ablated segments, but both upstream and downstream arteries continued to be perfused by arcading vessels from adjacent vascular trees (#2,4,6 and 9). Immediately after and at day 2 post-ablation, the segments near the ablations were not perfused. Nonetheless, at day 14, there is a signal of blood flow (Fig.5, D14 green arrowheads) confirming the data from bright field microscopy (green arrowheads in Fig.2, D630). The arteries upstream from the ablation (#2 and 4) have a decreased diameter and flow velocity during the first few days post-ablation while the more peripheral arteries (#6 and 9 with reversed flow as observed experimentally) increased their diameters from day 2 post-ablation and through day 14, suggesting that they are largely responsible for the compensatory flow being rerouted from the parallel arteries (which are outside of the field of the window).
Blood flow visualized by decorrelation-based quantitative flowmetry OCT before ablation (D0), just after ablation (D0+) and on days 2 (D2) and 14 (D14). The three ablation sites are marked with blue circles at D0- (see also Fig.2 D0 and D0+). Areas in the blue boxes at D0 and D14 (a, b) appear at bottom at higher magnification. Immediately post-ablation, flow is completely interrupted in the segments just downstream from the ablations and diverted to alternative pathways. The venous connection in left side ablation site (circle 1 in Fig.2 D0 and D0+) is reconnected by day 14 while the arterial segment is not reconstructed. The flow is reversed in artery 6 which received blood from the bottom vascular network from day 0 to day 30 when the direction of flow is restored to pre-ablation direction from the large artery segments 2 and 4 towards segment 6 (Supplementary Videos S1S3). Venous segment 10 remodels close to 400% from a venule to a major vein. Smaller post-capillary venules also appear to be involved in this rerouting of flow (arrowheads). By day 14, angiogenesis has partially reconnected the artery in this region, and some flow is evident (arrow, b).
The main vein (#1 and 3) significantly decreased its diameter on day 2 but by day 14 the main vein and its small branch (#10) as well as a contiguous series of microvessels became enlarged to match the size of the vein (Fig.5a,b). Venous branch #5 maintained its diameter throughout the 14-day time course, as its flow was not directly affected by the ablations, and exit flow proceeded through the main vein through this pathway. After the ablation, flow through vein #7 was rerouted through vein #8, causing flow reversal in this vessel (Supplemental video S3A). Once the connection between these segments and the main vein was reestablished, the flow direction in vein #8 returned to normal (Supplementary video S3B). These changes in flow direction and topology resulted in large changes in diameter and flow rate in this region (Fig.5, D14). A side branch, venule #10 was affected little by the ablations, and maintained exit flow through the main vein. The ablation completely stopped exit flow in vein #11 by day 14, the connection is rerouted, and flow and diameter are returning to pre-ablation levels.
Diameter measurements at later time points show that main artery segments #2 and 4 recover after the initial diameter decrease probably due to vasoconstriction caused by the ablation. They continue to remodel outwards from day 1628 with a transient dip at day 14 (Fig.6 top histograms). The transverse arteriole #6 diameter increased throughout the time course although the flow direction changed (Supplementary videos S2AC). Despite interruption from the main artery 52, its distal arteriole branch #9 had undergone outward remodeling (with a transient lower rate at day 13) due to collateral and reversed flow from adjacent arterioles.
Time course of diameter changes for the representative vessel segments imaged by OCT (see Fig.5). The venous connection in area 1 is re-established by day 14 while the arterial segment #2 is not reconstructed. The flow is reversed in artery 6 which received blood from the vessels of the distal network at the bottom region of the Figs. 1 and 2. Venous segment 10 remodels close to 400% from a precapillary venule to a major vein.
The main vein segments #1, 3 and 8 remodeled inward at early time points and then outward from day 14 on. The transverse venules #5 and 7 remodeled outward, likely to compensate for the main vein interruption. Interestingly, the distal part of the small venule #10 remodeled outward rapidly to match diameter and re-route flow to the main vein. Its diameter increased by 40% at day 6 to 221% at day 13, 229% at day 14, 306% at day 16 and 343% at day 20. Vessel #10s outward diameter remodeling peaked at day 23 at 379% increase from normal (close to 400%) and decreased by the end of the observation period at day 28282% of the original diameter at day 23, suggesting a possible transient remodeling (Figs. 5b, 6, venous segment #10).
The specific diameter changes and patterns of remodeling were observed in detail in five specimens. The present data demonstrated that microvascular remodeling patterns are similar and reproducible but differ in detail from mouse to mouse (sFig. 1 and sTables 26). The comprehensive data presented in sTable 6 not only underscores the diversity of remodeling patterns but also the intricate and adaptive nature of microvascular networks in response to laser ablation, offering valuable insights into their behavior and potential clinical relevance.
sFigure 1, in conjunction with sTables 26, provides a comprehensive insight into the dynamic behavior of microvascular networks in response to laser ablation. The figures and data within sFig. 1 offer a detailed visual representation of the time course and various remodeling patterns observed across five distinct animal experiments (Mice AE). These patterns include collateral outward remodeling, reopening of arterial and venous segments, and instances of permanent segment occlusion. We have noticed isolated tortuosity in some of the observed vessels (sFig. 1B day 5 and C day 18) although not as extensive as it was noticed before. The selection of Mouse E as a representative case for in-depth analysis in sFig. 1 serves to illustrate consistent changes seen across all mice while supplying essential anatomical data for subsequent biological and mathematical modeling endeavors. sTable 6 complements this by summarizing the observed remodeling patterns at different time points, highlighting the persistence of both outward and inward remodeling in arterial and venous segments throughout the observation period. Additionally, the findings emphasize the network's remarkable adaptability, with the ability to achieve persistent occlusion over the period of observation in some vessels while also demonstrating the capacity for gradual reopening over time in others. Together, sFig. 1 and sTable 6 could offer critical insights that have relevance for both experimental investigations and potential clinical applications.
We next investigated flow patterns in the network before and after the ablations. To do this, we used a computational approach to estimate flow in each segment. The first step in computational modeling is extraction of the network topology and characterization from bright field images taken with the stereo microscope (Fig.7). The venous network roughly parallels the arterial network with visibly larger diameter vessels. The direction of the flow for each segment was observed from the live BF microscopy recordings and marked on the network map (Fig.7a,b).
Vascular network topology and flow patterns. The arterial (a) and venous networks (b) are traced separately based on intravital images, and digitized versions are extracted. The observed flow directions are indicated by arrows.
We then used a simulated annealing method to estimate flow rates and pressures throughout the network (see Methods). Guesses are made for the terminal segment pressures, and the flows are calculated based on topology and measured vessel diameters. The predicted flow direction in each segment is compared to the observed direction, and an error function is calculated based on the number of incorrect directions. The error is used to scale a set of new guesses for the pressures, which is also subjected to a random function (this is the basis for the simulated annealing method). The process is then repeated to minimize the number of incorrect flow directions in individual segments. Using this method, we find that most large vessels have flow that varies little between trials (blue in Fig.8), but that flow direction in a few vessels (red in Fig.8) is relatively uncertainshowing a high sensitivity to distant changes in pressure. This suggests that these vessels can readily serve as collaterals that are available to redirect flow in either direction if necessary.
Computational model results of the pre-ablation network. Flow rates have been normalized relative to a value of 1000 assigned to largest vessel segment located on the left side. (AD) The histograms show the frequency of flow rates in representative vessel segments obtained from 100 runs of the simulated annealing algorithm. Numbers in the network map show the average flow rate for each segment, calculated over the 100 runs. The network map is color coded to show the relative uncertainty (standard deviation/mean) of the flow rates in each segment.
First, the flow distribution of individual vessels was optimized based on network topology and flow directions in the normal non-ablated state for vessels with different levels of uncertainty/flow levels (Fig.8). Before ablation, the larger arteries have low uncertainty, suggesting that they rarely change flow direction (Fig.8, blue and yellow color vessels). For example, the vessel fragment in Fig.8, panel C has a low level of uncertainty (indicated by blue color on the vessel map) and the relative values of the volumetric flow rate are mostly around 20% of that in the largest vessel (which is assumed at a value of 1000). The segments with the highest uncertainty mostly carry lower flow and are located near the center of the network (Fig.8, red and orange color vessels). To illustrate this point, vessel fragments in Fig.8, panels A, B and D have a higher uncertainty (orange and red on the vessel map) and therefore a wider range of possible values. Note that the segments in panels A and B stabilize at zero or close to zero values which reflects a low priority for these collateral vessels prior to ablation.
Using this method, we estimated flow through the network before (Fig.9A,C) and after ablation (Fig.9B,D) for arteries and veins, respectively. The venous network had more segments with higher flow rate pre-ablation (Fig.9, C vs. A). In arteries, after ablation, flow tends to be reversed in vessels with a high uncertainty index in the pre-ablation model close to the site of ablation (Fig.9). There was no flow reversal in the vein network although the flow magnitude was slightly changed in many vessel fragments.
Computational model results of the pre- (a and c) and post-ablation (b and d) networks. Numbers in the network maps indicate flow rate. The arterial network (a and b) had fewer fragments with high flow rate uncertainty than the venous network (c and d). However, flow reversal was common in the arteries but not the veins.
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New housing permits also declined heading into March at a rate of 1.46 million, 4.3% below the revised February rate of 1.52 million. They did, however, remain slightly above the March 2023 rate1.5% to be exact.
Builder sentiment overall remains unchanged, however, as many have a small level of optimism despite predictions that the rate of new homes being constructed expected to continue to drop.
According to NAHB Chief Economist Robert Dietz, Aprils flat reading suggests potential for demand growth is there, but buyers are hesitating until they can better gauge where interest rates are headed.
Despite experiencing a slight dip at the beginning of the year to below 7% on optimism of a March rate cut, mortgage rates have steadily continued to climb and are now moving back towards that 7% mark on news of persistent inflation in a hotter than expected market that may now push rate cuts back towards July at the earliest.
Currently, YoY inflation sits at 3.3%, still above the Feds target of 2%, which has led to a more conservative stance in introducing rate drops.
To combat the ongoing affordability issues, homebuilders have continued to offer price cuts and other incentives to first time home buyers to increase sales while also diversifying into smaller, more affordably built homes to account for the high price of materials.
The number of homebuilders cutting prices has dropped, however, to 22%, down from 24% last month. Sales incentives, likewise, have decreased from 60% down to 57% this month. The average price reduction of homes is remaining steady though at 6% for the tenth straight month.
Inventory, howeveror lack thereofcontinues to be the buoying force to homebuilder sentiment, as an overwhelming lack of existing homes has led to newly constructed housing being the primary source of home inventory for many homebuyers.
This lack of existing inventory has also led to similarly optimistic views on the remodeling market, with the NAHBs most recent Remodeling Market Index (RMI) giving a 66 reading (with 50 marking a neutral sentiment). The index runs from 0 to 100.
Demand for remodeling remains solid, especially among customers who dont need to finance their projects at current interest rates, said NAHB Remodelers Chair Mike Pressgrove, a remodeler from Topeka, Kan. Construction costs are still an issue in some places, just as they were toward the end of last year.
The Current Conditions Index also averaged 74, remaining unchanged from the previous quarter and indicating strong positive sentiment overall for the demand of remodeling projects across all price ranges which include $50,000; $20,000 $50,000; and below $20,000.
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Excerpt from:
New Housing Starts Plunge in March, Remodeling Sentiment Remains Positive - CE Pro
