Additional file 1: Supplementary Spreadsheets and Figures. All spreadsheets for marker genes contain the following columns: p-value (pval), average log2 fold-change (avg_log2FC), percent of cells in the cluster expressing the marker (pct.1), percent of cells outside the cluster expressing the marker (pct.2), the multiple test corrected p-value (p_val_adj), the cluster number (cluster), the gene name (gene), the flybase id (Fbgn_ID), gene long name (GeneName), datestamp of flybase snapshot inclusion (datestamp) and the Flybase gene snapshot for the gene in question, when available.(gene_snapshot_text). Supplementary_spreadsheet_1_Time_and_tissue_breakdown.ods. Spreadsheet detailing the number of cells per cluster and sample of origin, a stage by cell number breakdown and sequencing quality control metrics for each sequenced sample. Supplementary_spreadsheet_2_Ncells_and_gene_markers_per_cluster.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers resulting from running the FindAllMarkers algorithm as detailed in the methods. An additional sheet contains the number of cells per cluster. Supplementary_spreadsheet_3_Ncells_and_gene_markers_per_cluster_and_stage.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers at each stage, ie. 1h, 24h and 48h resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis. An additional sheet contains the number of cells per cluster at each stage. Supplementary_spreadsheet_4_Ncells_and_gene_markers_per_cluster_and_tissue.xlsx. Spreadsheet containing one sheet per detected cluster with all the cluster defining markers for each tissue, ie. brain, CNS and VNC, resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis. An additional sheet contains the number of cells per cluster detected in each tissue dissection. Supplementary_spreadsheet_5_Differential_expression_cluster_mature_neuron_classes.ods. Spreadsheet containing one sheet per mature neuron subtype and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to mature cell-types only: Cholinergic, Gabaergic, Glutamatergic, Kenyon Cells, Motor, Monoaminergic and Peptidergic neurons. Supplementary_spreadsheet_6_Differential_expression_cluster_big_classes.ods. Spreadsheet containing one sheet per major cell-type class and their defining markers: Immature neurons, Cholinergic neurons, Neuroprecursor cells, Gabaergic neurons, Glutamatergic neurons, Kenyon cells, Unknown neurons, Motorneurons, Glia, Hemocytes, Epithelia/trachea, Monoaminergic neurons, Peptidergic neurons and Ring Gland. Supplementary_spreadsheet_7_NPCs_markers_among.xlsx. Spreadsheet containing one sheet per Neuroprecursor cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Neuroprecursor clusters only. Supplementary_spreadsheet_8_Immature_neuron_markers_among.xlsx. Spreadsheet containing one sheet per Immature neuron cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Immature neuron clusters only. Supplementary_spreadsheet_9_Cholinergic_markers_among.xlsx. Spreadsheet containing one sheet per Cholinergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Cholinergic clusters only. Supplementary_spreadsheet_10_Gabaergic_markers_among.xlsx. Spreadsheet containing one sheet per Gabaergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Gabaergic clusters only. Supplementary_spreadsheet_11_Glutamatergic_markers_among.xlsx. Spreadsheet containing one sheet per Glutamatergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Glutamatergic clusters only. Supplementary_spreadsheet_12_Octopaminergic_markers_among.xlsx. Spreadsheet containing one sheet per Octopaminergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Octopaminergic clusters only. Supplementary_spreadsheet_13_Serotoninergic_markers_among.xlsx. Spreadsheet containing one sheet per Serotoninergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Serotoninergic clusters only. Supplementary_spreadsheet_14_Peptidergic_markers_among.xlsx. Spreadsheet containing one sheet per Peptidergic cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Peptidergic clusters only. Supplementary_spreadsheet_15_Kenyon-Cells_markers_among.xlsx. Spreadsheet containing one sheet per Kenyon cells cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Kenyon cells clusters only. Supplementary_spreadsheet_16_Glia_markers_among.xlsx. Spreadsheet containing one sheet per Glia cluster and their markers resulting from running the FindAllMarkers algorithm as detailed in the methods but restricting it to Glia clusters only. Supplementary_spreadsheet_17_Enriched_markers_per_cluster_48_vs_24h.xlsx. Spreadsheet containing one sheet per big cell-type class with all the markers enriched at 48h vs 24h resulting from running the FindAllMarkers algorithm as detailed in the methods for the temporal analysis but restricting it to 48 vs 24h. Supplementary_spreadsheet_18_selective_one_per_class_075-19.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. Supplementary_spreadsheet_19_Identity_markers_and_refs.ods. Spreadsheet containing the list of all markers used to identify cell classes together with literature references. Supplementary_spreadsheet_20_Brain_only_atlas_markers.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. In the Brain samples and the VNC samples it can be seen that there is a drastic increase of immature neurons relative to mature neurons from 24 hrs to 48 hrs. In the Brain samples, at 24 hrs, the ratio of immature (4885) to mature neurons (8536) is 0.57; at 48 hrs the ratio of immature (12092) to mature neurons (9758) is 1.23 (2.2-fold increase). Supplementary_spreadsheet_21_VNC_only_atlas_markers.xlsx. Spreadsheet containing one sheet per cluster with all markers selective for that cluster when imposing a cut-off of log2 fold-change greater than 0.75 and the requirement of being detected in more than 19% of cells. In the Brain samples and the VNC samples it can be seen that there is a drastic increase of immature neurons relative to mature neurons from 24 hrs to 48 hrs. In the VNC samples, at 24 hrs, the ratio of immature (3146) to mature neurons (4885) is 0.64; At 48 hrs the ratio of mature (2173) to immature (3513) is 1.61 (2.5-fold increase). Supplementary_Figure_1_UMAP_plot_per_tissue.pdf. UMAP representation of the CNS cell type diversity discovered after reciprocal-PCA integration, dimensionality reduction and unsupervised clustering with Seurat and split by tissue of origin. In this 2D representation each dot represents a cell and their distribution in space is a function of their similarity in gene expression profile. Each cluster is color and number coded as depicted in the accompanying legend. Supplementary_Figure_2_Brain_independent_analysis.pdf. UMAP dimensional reduction plot with the annotated clustering resulting from the analysis of VNC samples only at 24 and 48h. Supplementary_Figure_3_VNC_independent_analysis.pdf. UMAP dimensional reduction plot with the annotated clustering resulting from the analysis of Brain samples only at 24 and 48h. Supplementary_Figure_4_endogenous-nSyb-feature_plot.pdf. Feature plot comparing the expression distribution of endogenous and UAS-GAL4 amplified expression of nSyb. Supplementary_Figure_5_feature_plot_nSyb_Repo_Notch.pdf. UMAP dimensional reduction showing the expression distribution of endogenous nSyb, repo and Notch. In this 2D representation each dot represents a cell and their distribution in space is a function of their similarity in gene expression profile. Color represents the expression of the gene for that particular cell. In each dotplot, the centered mean expression of a gene for each class is calculated and given a color ranging from blue (lowest expression) to red (highest expression), with white corresponding to 0. In this fashion different genes can be compared by their relative expression in the classes depicted irrespective of their absolute expression levels. The diameter of each dot is proportional to the number of cells expressing that gene in the class. Supplementary_Figure_6_cholinergic_markers_dotplot.pdf. Dotplot depicting Cholinergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_7_glutamatergic_markers_dotplot.pdf. Dotplot depicting Glutamatergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster. Supplementary_Figure_8_gabaergic_markers_dotplot.pdf. Dotplot depicting Gabaergic markers showing an average log2 fold-change greater than one compared to the other clusters and present in at least more than 19% of the cells of the cluster.

List of marker genes that were identified using the FindAllMarkers function for all non-immune clusters as shown in Fig 5. Table contains the gene name (rowname and gene), the p-value (p_val), the average log2 fold change compared to all other clusters (avg_log2FC), percent of cells expressing the gene in cluster of interest (pct.1), percent of cells expressing the genes in all other clusters (pct. 2), adjusted p-value (p_val_adj) and the cluster to which the gene belongs (cluster). (XLSX)

List of marker genes that were identified using the FindAllMarkers function for all myeloid clusters as shown in Fig 4. Table contains the gene name (rowname and gene), the p-value (p_val), the average log2 fold change compared to all other clusters (avg_log2FC), percent of cells expressing the gene in cluster of interest (pct.1), percent of cells expressing the genes in all other clusters (pct. 2), adjusted p-value (p_val_adj) and the cluster to which the gene belongs (cluster). (XLSX)

Hematopoietic cells were stained with fluorochrome-conjugated antibodies against human CD45, CD3, CD19 and CD14 and stromal cells with fluorochrome-conjugated antibodies against human CD45 and CD235a. Live/dead cell discrimination was performed by adding 7-amino-actinomycin D (7AAD; Calbiochem) prior to acquisition. CD45– CD235a– stromal cells, CD45+ CD3+ T cells and CD45+ CD19+ B cells were sorted with a BD FACS Melody cell sorter (BD Biosciences) and run on the 10x Chromium analyzer (10X Genomics). cDNA library generation was performed following the established commercial protocol for Chromium Single Cell 3’ Reagent Kit (v3 Chemistry). Libraries were run via Novaseq 6000 for Illumina sequencing at the Functional Genomic Center Zurich. A total of 20 samples were collected from 9 patients and processed in 6 batches. All samples from the same patient were processed in the same batch. Gene expression estimation from sequencing files was done using CellRanger (v3.0.2) count with Ensembl GRCh38.9 release as reference to build the index for human samples. Next, quality control was performed in R v.4.0.0 using the R/Bioconductor package scater (v.1.16.0) and included removal of damaged and contaminating cells based on (1) very high or low UMI counts (>2.5 median absolute deviation from the median across all cells), (2) very high or low total number of detected genes (>2.5 median absolute deviation from the median across all cells) and (3) high mitochondrial gene content (> 2.5 median absolute deviations above the median across all cells). In addition, contaminating cells expressing any of the markers CD3E, PTPRC, CD79A or GYPA were removed from stromal cell samples. Downstream analysis was performed using the Seurat R package (v.4.0.1) and included normalization, scaling, dimensionality reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers as well as dimensionality reduction with diffusionmap as implemented in the scater R/Bioconductor package (v.1.16.0). Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes as reported in previous publications. For the extended stromal cell analysis, two contaminating clusters with 50 cycling cells and 150 cells expressing both fibroblast and endothelial marker genes (indicative of doublets) were removed. For high resolution FRC analysis, FRC subsets were re-embedded and two clusters containing 256 cells with high levels of endothelial or mitochondrial/non-coding genes, respectively, were excluded. Comparative analysis included determination of cell type-, subset- and condition-specific gene signatures. Thereby differentially expressed genes were calculated running the FindAllMarkers function from Seurat R package.

List of marker genes that were identified using the FindAllMarkers function for all lymphoid clusters as shown in Fig 3. Table contains the gene name (rowname and gene), the p-value (p_val), the average log2 fold change compared to all other clusters (avg_log2FC), percent of cells expressing the gene in cluster of interest (pct.1), percent of cells expressing the genes in all other clusters (pct. 2), adjusted p-value (p_val_adj) and the cluster to which the gene belongs (cluster) (XLSX)

Here, we provide:Robjects pertaining to scRNA-seq (Seurat) and snATAC-seq (Signac) analysis. These contain the single-cell and single-nuclei used in downstream analyses. Tables containing information about the gene markers identified for each cluster in scRNA-seq, peak markers identified for each cluster in snATAC-seq, and motif enrichment analyses using chromVAR motif scores. Differential gene expression and motif enrichment analyses was performed using Wilcoxon rank sum test comparing the distribution of gene expression or chromVAR motif scores between cells in the cluster and all other cells. Differential peak analyses was performed using FindAllMarkers in Signac.

Markers included have a log-fold above 1 and expressed in at least 50% of the cluster cells. In all the rest of the tabs: Markers for each of cell subtype (Myeloid, T/NK cells, vascular/lymphatic endothelial cells, enterocytes, and fibroblasts) clusters (compared internally to the other clusters in the same cell subtype), identified by the FindAllMarkers command in Seurat. Markers included have a log-fold above 0.6 and expressed in at least 25% of the cluster cells. (XLSX)

Droplet-based single-cell and single nucleus RNA sequencing analysis of murine heartsTo obtain sufficient numbers of cells from all cardiac cell types, a total of n=14 samples (WT: 4 samples; TCRM: 6 samples; TCRM isotype: 2 samples; TCRM 14-D10-2: 2 samples) from mouse hearts were processed for single cell RNA sequencing, and n=9 samples (WT: 2 samples; TCRM: 3 samples; TCRM isotype: 2 samples; TCRM 14-D10-2: 2 samples) were prepared for single nucleus RNA sequencing analysis. Samples were processed and sequenced in n=3 batches with all batches spanning multiple conditions. Single cell suspensions were run using the 10x Chromium (10x Genomics) system. The cDNA libraries were generated according to the established commercial protocols for Chromium Single Cell 3’ Reagent Kit (NextGem Chemistry) and Chromium Nuclei Isolation Kit. All libraries were sequenced by NovaSeq 6000 Illumina sequencing at the Functional Genomic Center Zurich. Gene expression was analyzed from sequencing data using CellRanger (v.5.0.1) count, with Ensembl GRCm38.9 as reference. Next, quality control was carried out in R (v.4.2.1) using the R/Bioconductor packages scater (v.1.24.0) and SingleCellExperiment (v.1.18.0) packages. This involved the identification and removal of damaged cells/nuclei or doublets, based on criteria including unusual UMI or gene counts (>2.5 median absolute deviation from the median across all cells) and high mitochondrial gene content (> 2.5 median absolute deviations above the median across all cells). After performing quality control, the final dataset included 31 078 cells and 24 995 nuclei.Downstream analysis was performed using the Seurat R package (v.4.1.1). First, all samples were merged and integrated across data type (single cell or single nucleus data) using the IntegrateData function from the Seurat R package to account for differences between single cell and single nucleus data. Downstream analysis further included normalization, scaling, dimensional reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers. Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes as reported in previous publications (refs). In order to examine expression signatures of Fibroblasts in more detail cells assigned as Fibroblasts were re-embedded and re-analysed individually.Droplet-based single nucleus RNA sequencing analysis of human heart biopsiesAs for murine samples, isolated nuclei from human heart biopsies were run using the 10x Chromium (10x Genomics) system and cDNA libraries were generated according to the established commercial protocols for Chromium Single Cell 3’ Reagent Kit (NextGem Chemistry) and Chromium Nuclei Isolation Kit. Libraries were sequenced by NovaSeq 6000 Illumina sequencing at the Functional Genomic Center Zurich and gene expression was estimated using CellRanger (v.5.0.1) count, with Ensembl GRCh38.103 as reference. Quality control included the removal of nuclei with unusual UMI or gene counts (>2.5 median absolute deviation from the median across all cells) and was performed in R v.4.2.1 using the R/Bioconductor packages scater (v.1.24.0) and SingleCellExperiment (v.1.18.0).For downstream analysis with the Seurat R package (v.4.3.0) all samples were merged and integrated across patient ID using the IntegrateData function. Integrated data was further processed running normalization, scaling, dimensional reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers. Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes as reported in previous publications. Following cluster assignments samples were grouped based on their T cell proportions and groups were compared by calculating differentially expressed genes using the FindAllMarkers function from the Seurat R package.

Most of the methodologies to detect differences using omic-wide data in biomedical studies (i.e. methods like SAM, LIMMA or the simple t-test) are based on the analyses of significant mean changes to calculate the differential signal (i.e. gene expression signal) between classes of samples. All these methods perform correctly when biomarkers do not show heterogeneous behavior within each group. However, the biological signal measured can be often altered in only a subset of clinical samples due to multiple sources of undefined variance within individuals of the same class. In addition, the classification of patients within a specific pathological subtype according to their phenotype can carry errors due to the methodology used in their clinical exploration and study. Thus, samples from each category may be mixed or wrongly labeled due to the existence of a hidden category. We are developing and evaluating in R language an algorithm to address this point of possible wrong class labeling and to identify specific markers for each class between closely related biological/clinical states. To do so, the method builds an incidence matrix that contains the frequency with which the variables (genes) have differential signal (expression), considering different subsets of all the clinical conditions. Based on this matrix, the method performs a correspondence analysis to represent in the same space the genes and the samples, and later a cluster analysis to find highly significant associations between genes and samples. Finally, the method implements a score to each gene covering both the significance of its differential signal and the number of samples with which it is associated.

The study of neurodegenerative diseases, particularly tauopathies like Pick’s disease (PiD) and Alzheimer’s disease (AD), offers insights into the underlying regulatory mechanisms. By investigating transcriptomic and epigenomic variations in these conditions, we identified critical regulatory changes driving disease progression, revealing potential therapeutic targets. Our comparative analyses uncovered disease-enriched non-coding regions and genome-wide transcription factor (TF) binding differences, linking them to target genes. Notably, we identified a distal human-gained enhancer (HGE) associated with E3 ubiquitin ligase (UBE3A), highlighting disease-specific regulatory alterations. Additionally, fine-mapping of AD risk genes uncovered loci enriched in microglial enhancers and accessible in other cell types. Shared and distinct TF binding patterns were observed in neurons and glial cells across PiD and AD. We validated our findings using CRISPR to excise a predicted enhancer region i..., , , # Single-nucleus multi-omics identifies shared and distinct pathways in Pick’s and Alzheimer’s disease

https://doi.org/10.5061/dryad.h9w0vt4t9

Description of the data and file structure

To ensure compliance with data protection policies, all personally identifiable information (PII) has been removed. Metadata fields that could potentially be used to identify individuals—such as Brainbank name, exact Age, Sample ID, and Postmortem Interval (PMI)—have been masked or anonymized in the accompanying metadata and README file.

This dataset contains only de-identified data and is shared in accordance with the ethical standards of the contributing institutions and the data sharing policy of DataDryad.org.

We extracted replicable markers for neuron cell types from a compendium of 7 scRNAseq datasets generated by the BICCN in the mouse primary motor cortex (https://doi.org/10.1101/2020.02.29.970558). The markers were extracted using the MetaMarkers package (https://github.com/gillislab/MetaMarkers) using default parameters and keeping the top 1000 markers for each cell type.We present markers for cell types at each level of the hierarchy defined by the BICCN. - biccn_class_markers.csv: highest level with only 3 cell types (excitatory neurons, inhibitory neurons, non-neurons). - biccn_subclass_markers.csv: intermediate level containing 13 cell types (e.g. PV+ interneurons, L6b excitatory neurons). - biccn_cluster_markers.csv: high-resolution level containing 86 cell types (e.g. Chandelier cells).The number of informative markers varies by cell type. To find the best number of markers, we looked for optimal annotation performance for each cell type. The results are summarized in "optimal_number_markers.csv". For each cell type, performance is reported in column "f1" (0.75 indicates good performance, 1 is perfect performance). To read this table, pick a cell type of interest, find optimal performance, then the range of genes that lead to optimal performance. For example, Chandelier cells (Pvalb Vipr2_2) are perfectly characterized by 50 to 200 markers (F1>0.99).

The file Panagiotopoulou_etal_Abaerii_sex_markers_RNAseq_search.bz2 contains results of RNAseq analyses of the female and male transcriptomes of Siberian Sturgeon (Acipenser baerii). Total RNAs were isolated from fin tissues and subjected to Whole Transcriptome Sequencing (WTS) on Illumina platform. Compressed file contains assemblies of the sequences, their annotations and results of the differential expression analyses of the two transcriptomes representing both sexes. Files were designated respectively to the origin of RNAs (female sequences are in files containing in their names string “Aci_2”, male sequences are in files containing in their names string “Aci_I”). File designated All-Unigene.fa contains full transcriptome of Siberian Sturgeon – joined female and male transcriptomes without redundant sequences. All sub-folders contains text and html files to facilitate data exploration.Sturgeons belong to an extraordinarily old and highly endangered group of fish. Although the sex determining region in this taxon was recently discovered, there is still a great need for the development of reliable gene expression markers for sturgeon sexing in their different life stages which could be easily applied in field conditions. In this study an extensive screening of the Atlantic sturgeon genome was performed using: AFLP (202 PC) and MS-AFLP (256 PC) markers likewise the Siberian sturgeon transcripts deriving from the Whole Transcriptome Sequencing (WTS) and cDNA-AFLP (128 PC). Genetic material extracted from female and male fins was used. AFLP reactions were minimized and automatized and a novel method for SCAR identification was proposed (SCAR-NGS). The 204 primary selected markers were subjected to gDNA or cDNA PCR amplifications, semi- and RT-qPCRs in order to confirm the presence of unique sex-marker sequences in the genomes or sex-linked expression differences. We identified eleven loci in the Atlantic sturgeon genome which exhibited sex-linked polymorphisms of the restriction sites (SNP based). In case of the Siberian sturgeon, 41 transcripts were confirmed to be expressed in a sex specific manner; three of which were also significantly differentially amplified in the female and male genomes. Six of those markers were highly overexpressed in males, which was quantified using RT-qPCRs. The approach enabled the discovery of DNA and RNA candidate sequences suitable for application in sturgeon sexing, being important both for commercial aquaculture optimization and genetic variability maintenance of sturgeons.

DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range. Methods Metabarcoding markers Twenty-two markers for mitochondrial (COI, 12S, 16S) and nuclear (18S, 28S) barcoding genes were selected from metabarcoding, eDNA, and Sanger sequencing barcoding studies of marine and freshwater fishes, including seafood products (Table 1). Most of these markers were designed to target bony fishes (teleosts), but we added markers targeting elasmobranchs, crustaceans, and cephalopods – taxonomic groups that are often poorly resolved by universal fish barcodes. Only markers that amplified targets <300 bp were selected because shorter fragments are more likely to amplify degraded DNA (Devloo-Delva et al., 2019, Shokralla et al., 2015; Staats et al., 2016), which is expected to be the case for highly-processed fishmeal and oil. Reference DNA pools To compare the amplification and resolution of the 22 markers before determining complementarity, we constructed two pools with equal concentrations of extracted DNA from 98 marine and freshwater teleost fishes and five elasmobranch, crustacean, and cephalopod species, in total spanning 88 genera and 60 families (full reference pool; Table 2). Samples were obtained primarily from vouchered collections, but also from fish markets to encompass commercially-important groups. We sampled muscle tissue from inside the body wall (i.e., no surface contact) for DNA extractions, in an attempt to avoid trace contamination from contact with other species. To further minimize the potential for detecting false positives from tissue contamination, we constructed a second, more restricted reference pool including only the 73 DNA extracts from vouchered museum specimens (vouchered reference pool). Experimental tissue mixture samples Metabarcoding is typically used to detect both rare and abundant constituents in mixtures, and most applications include species in unequal proportions along with varying amounts and types of non-target material. In aquaculture feeds, we will refer to the non-target material as “filler.” To evaluate detection power in actual tissue mixtures (as opposed to pools of DNA extracts), we used fishmeal mixed with different fillers. The purpose of the filler was to test whether metabarcoding data are negatively impacted by fillers, either because of a loss of on-target sequencing reads or because of potential PCR inhibition. Similarly complex and heterogeneous mixtures of tissue sources might be expected in gut content or fecal samples in more ecological applications. To create experimental fishmeal, we freeze-dried tissue from 30 of the unvouchered fish species in the full reference pool (muscle tissue from market samples; whole fish from research samples), coarsely homogenized each sample in a coffee grinder, and then finely ground using a freezer mill where each tissue sample is pulverized within a container submerged in liquid nitrogen. Each species was added one-by-one, and we cleaned all containers and tools by wiping them with a 10% bleach solution followed by 70% ethanol to decontaminate between samples. Species were assigned to one of six abundance levels: 13.33%, 3.65%, 1.91%, 1%, 0.1%, or 0.01% of the mixture (by weight), thereby spanning >3 orders of magnitude variation in representation (Table 3). Each abundance level was represented by five species, which were assigned to balance freshwater and marine habitats, major phylogenetic groups, and degree of fishery interest across levels. This experimental design allowed us to assess how dominant and rare taxa added at discrete proportions to a heterogeneous mixture relate to the proportion of sequencing reads attributed to each taxon and to compare amplification biases across multiple taxa added in the same amount to the fishmeal. To test the effect of the non-target material, the fishmeal mixtures were combined with two unique fillers for a total of seven individual experimental feeds with low (2%), medium (10%), and high (25%) proportions of fishmeal relative to filler (Table 3). Fillers included plant-derived materials – grain and grass flours – and animal byproducts – bloodmeal and feathermeal – to represent mixture constituents used in aquaculture feeds. Fishmeal proportions also mimicked potential levels of fish tissue added to aquaculture feeds, from low (0%-2%) to high (25%) proportions of fish in the feed mixture. By multiplying the proportion of fishmeal in the experimental feed by the proportion of a particular fish species in the fishmeal, we could test the detection threshold for individual taxa down to 0.0002% of total experimental mixture mass (i.e., minimum of 0.01% of a particular species in the fishmeal and 2% fishmeal in the feed). DNA extracts were quantified by a Qubit fluorometer (high-sensitivity or broad-range dsDNA assay depending on concentration range), diluted with DNAse-free water, and added in equal proportion to the full reference and vouchered reference DNA pools. DNA extracts from the 30 fishmeal species were combined in two additional mock DNA pools: one with equal concentration among all taxa (mock equal) and the other in which DNA extract concentration was proportionate to the amount of tissue included in the fishmeal (mock variable). Similar to the previous reference DNA pools, DNA pools for the mock equal and mock variable pools were prepared in triplicate (Fig. S1). Metabarcoding sequencing libraries were prepared from each pool using a two-step amplicon protocol (D’Aloia, Bogdanowicz, Harrison, & Buston, 2017) in which an initial PCR targets the gene region of interest using locus-specific primers with Nextera 5’ tails (5’-TC GTCGGCAGCGTCAGATGTGTATAAGAGACAG appended to each forward primer and 5’ -GTCTCGTGGGCTC GGAGATGTGTATAAGAGACAG to each reverse primer, details in the SI). Equal volumes of the locus-specific PCR products for each sample were pooled and a second PCR added Nextera-style sequencing adapters with unique i5 and i7 indexes that allow sequencing reads to be assigned to samples during analysis (details about reagent concentrations and PCR conditions in the SI). Rather than using combinatorial indexing, which can lead to mis-assigned reads caused by index-swapping (Carøe & Bohmann, 2020; Schnell, Bohmann, & Gilbert, 2015), we used custom-synthesized adapters with unique dual indexes (Table S1) that can unequivocally identify samples by 8-base indexes on both ends of the molecule. For each sample, PCR products for all markers were pooled into a single indexed library and sequenced using paired-end 150-bp on one lane of a HiSeq X Ten (Novogene, Inc.) with 15% PhiX to account for moderately low library complexity (following Aizpurua et al., 2018).

Cell lines and compounds PCa cell lines (LNCaP, 22Rv1, VCaP, PC3, DU145, NCI-H660, C4-2), other cell lines (HEK293T, DLD1) and benign prostate line (RWPE-1) were purchased from ATCC and maintained according to ATCC protocols. Patient-derived CRPC organoids (WCM and MSK) were established and maintained as organoids in Matrigel drops according to the previously described protocol70. LNCaP-AR cells were a kind gift from Dr. Sawyers and Dr. Mu (Memorial Sloan Kettering Cancer Center) and were cultured as previously described5. All used cell lines and their phenotype are listed in Supplementary Table 1. Cell cultures were regularly tested for Mycoplasma contamination and confirmed to be negative. Genentech Inc. synthesized A947, its epimer (A858), FHD-286 and AU-15330. Cobimetinib, Trametinib, VL285 and CHIR99021 were purchased from SelleckChem. BRM014 was purchased from MedChemExpress. All drugs used in this study are listed in Supplementary Table 2. Single-cell RNA-sequencing by SORT-seq library generation and analysis SORT-seq was performed using Single Cell Discoveries (SCD) service. Organoids were treated for 72h with a control epimer (A858) or active compound (A947) at 1 µM, and 1x10e6 cells were harvested in PBS. Harvested cells were stained with 100ng/ml DAPI to stain dead cells. Using a cell sorter (conducted by Flow Cytometry Core, DBMR, Bern) and the recommended settings (Single Cell Discoveries B.V.), DAPI-negative cells were sorted as single cells in 376 wells of four 384-well plates containing immersion oil per condition. Resulting in a theoretical cell number of 1504 cells per condition. All post-harvesting steps were performed at 4°C. Plates were snap-frozen on dry ice for 15 minutes and sent out for sequencing at Single Cell Discoveries B.V. Data were analyzed using the Seurat package v.4.3.080. Cell QC filtering was done using the following thresholds: nCount > 4000, nFeature > 1000, percent.mito 0.85. Differential gene expression analysis between clusters was done with Seurat::FindAllMarkers. Module scores were generated with Seurat::AddModuleScore. Gene set enrichment analysis was done with the package fgsea v.1.24.081 and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Gene regulatory networks analysis was done with pySCENIC v.0.12.182. Overall analysis was done in R v.4.2.2. RNA-seq library generation and processing For bulk RNA-seq, organoids were treated with A858 or A947 (1µM) for 24h and 48h (3 biological replicates per condition). RNA was extracted using the RNeasy Kit (Qiagen); library generation and subsequent sequencing was performed by the clinical genomics lab (CGL) at the University of Bern. Sequencing reads were aligned against the human genome hg38 with STAR v.2.7.3a83. Gene counts were generated with RSEM v.1.3.284, whose index was generated using the GENCODE v33 primary assembly annotation. Differential gene expression analysis was done with DESeq2 v.1.34.085. Gene set enrichment analysis was done with the package fgsea v.1.20.081 and the human gene sets from the Molecular Signatures Database (https://www.gsea-msigdb.org). Analysis was done in R v.4.1.2. TCF7L2 ChIP-seq library generation and processing For the ChIP-Seq assay, chromatin was prepared from 2 biological replicates of WCM1078 treated with A858 or A947 (1µM) for 4h, and ChIP-Seq assays were then performed by Active Motif Inc. using an antibody against TCF7L2 (Cell Signalling, cat#2569). ChIP-seq sequence data was processed using an ENCODE-DC/chip-seq-pipeline2 -based workflow (https://github.com/ENCODE-DCC/chip-seq-pipeline2). Briefly, fastq files were aligned on the hg38 human genome reference using Bowtie2 (v2.2.6) followed by alignment sorting (samtools v1.7) of resulting bam files with filtering out of unmapped reads and keeping reads with mapping quality higher than 30. Duplicates were removed with Picard’s MarkDuplicates (v1.126) function, followed by indexation of resulting bam files with samtools. For each bam file, genome coverage was computed with bedtools (v2.26.0), followed by the generation of bigwig (wigToBigWig v377) files. Peaks were called with macs2 (v2.2.4) for each treatment sample using a pooled input alignment (.bam file) as control. Downstream analyses were performed with DiffBind v3.11.1 with default parameters, except for summits=250 in dba.count(). dba.contrast() and dba.analyzed() were used to compute significant differential peaks with DESeq2. ATAC-seq library generation and processing ATAC-seq was performed from 50’000 cryo-preserved cells per condition (1µM A858 and 1µM A947, n = 3 biological replicates) treated for 4h and analyzed as described in previous study86. Briefly, 50,000 cryo-preserved cells per condition were lysed for 5 minutes on ice and tagmented for 30 minutes at 37°C, followed by DNA isolation. DNA was barcoded and amplified before sequencing. PRO-cap library generation and processing For PRO-cap, approximately ...

Development of the dorsal aorta is a key step in the establishment of the adult blood-forming system since hematopoietic stem and progenitor cells (HSPCs) arise from ventral aortic endothelium in all vertebrate animals studied. Work in zebrafish has demonstrated that arterial and venous endothelial precursors arise from distinct subsets of lateral plate mesoderm. Here, we profile the transcriptome of the earliest detectable endothelial cells (ECs) during zebrafish embryogenesis to demonstrate that tissue-specific EC programs initiate much earlier than previously appreciated, by the end of gastrulation. Classic studies in the chick embryo showed that paraxial mesoderm generates a subset of somite-derived endothelial cells (SDECs) that incorporate into the dorsal aorta to replace HSPCs as they exit the aorta and enter circulation. We describe a conserved program in the zebrafish, where a rare population of endothelial precursors delaminates from the dermomyotome to incorporate exclusively into the developing dorsal aorta. Although SDECs lack hematopoietic potential, they act as a local niche to support the emergence of HSPCs from neighboring hemogenic endothelium. Thus, at least three subsets of ECs contribute to the developing dorsal aorta: vascular ECs, hemogenic ECs, and SDECs. Taken together, our findings indicate that the distinct spatial origins of endothelial precursors dictate different cellular potentials within the developing dorsal aorta. Methods Single-cell RNA sample preparation After FACS, total cell concentration and viability were ascertained using a TC20 Automated Cell Counter (Bio-Rad). Samples were then resuspended in 1XPBS with 10% BSA at a concentration between 800-3000 per ml. Samples were loaded on the 10X Chromium system and processed as per manufacturer’s instructions (10X Genomics). Single cell libraries were prepared as per the manufacturer’s instructions using the Single Cell 3’ Reagent Kit v2 (10X Genomics). Single cell RNA-seq libraries and barcode amplicons were sequenced on an Illumina HiSeq platform. Single-cell RNA sequencing analysis The Chromium 3’ sequencing libraries were generated using Chromium Single Cell 3’ Chip kit v3 and sequenced with (actually, I don’t know:( what instrument was used?). The Ilumina FASTQ files were used to generate filtered matrices using CellRanger (10X Genomics) with default parameters and imported into R for exploration and statistical analysis using a Seurat package (La Manno et al., 2018). Counts were normalized according to total expression, multiplied by a scale factor (10,000), and log-transformed. For cell cluster identification and visualization, gene expression values were also scaled according to highly variable genes after controlling for unwanted variation generated by sample identity. Cell clusters were identified based on UMAP of the first 14 principal components of PCA using Seurat’s method, Find Clusters, with an original Louvain algorithm and resolution parameter value 0.5. To find cluster marker genes, Seurat’s method, FindAllMarkers. Only genes exhibiting significant (adjusted p-value < 0.05) a minimal average absolute log2-fold change of 0.2 between each of the clusters and the rest of the dataset were considered as differentially expressed. To merge individual datasets and to remove batch effects, Seurat v3 Integration and Label Transfer standard workflow (Stuart et al., 2019)

The output of FindAllMarkers function in Seurat was printed as a table. (XLSX)

Oil Based Marker Pen Market Outlook

The global oil based marker pen market size was valued at approximately USD 1.2 billion in 2023 and is expected to reach around USD 2.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. This growth is driven by increasing demand across various applications such as art and design, industrial usage, and office supplies. The versatility and durability of oil-based marker pens make them a preferred choice for professionals and hobbyists alike, contributing significantly to market growth.

One of the primary growth factors for the oil based marker pen market is the rising popularity of art and design activities. With a surge in creative arts, both as a professional field and a personal hobby, there is an increased demand for high-quality, reliable art supplies. Oil-based markers are particularly favored for their vibrant colors and long-lasting ink, making them a staple in both professional artist studios and amateur art supplies. Additionally, the growing influence of social media platforms has amplified the visibility of art and craft projects, further fueling the demand for premium art materials, including oil-based marker pens.

Another significant growth driver is the industrial application of oil-based marker pens. These markers are extensively used in various industries for marking on surfaces like metal, glass, and plastic, where regular markers would fail. The durability of oil-based ink, which is resistant to water and fading, makes these markers indispensable in settings where permanence and clarity are crucial. The booming manufacturing and construction sectors are particularly notable consumers of these products, as they rely on them for labeling and coding purposes, thereby directly impacting market expansion.

The office and school supplies segment also plays a vital role in the market growth of oil-based marker pens. With a global increase in the number of educational institutions and offices, there is a consistent demand for reliable writing instruments. Oil-based markers offer an edge over water-based markers due to their longevity and smudge-proof characteristics, making them a preferred choice for official documentation and educational activities. This steady demand from the office and educational sectors is expected to sustain the market growth over the forecast period.

Regionally, Asia Pacific is anticipated to dominate the market, driven by rapid industrialization and a growing emphasis on education. Countries like China and India are witnessing significant investments in infrastructure and education, leading to heightened demand for industrial and educational supplies, including oil-based marker pens. Additionally, the presence of numerous local and international manufacturers in this region is expected to boost market growth. In contrast, North America and Europe are likely to see moderate growth, driven by stable demand from the art and design sectors and continuous advancements in marker pen technology.

Product Type Analysis

The oil based marker pen market is segmented by product type into fine tip, medium tip, and broad tip markers. Fine tip markers are widely used for detailed work, making them indispensable for artists, designers, and professionals who require precision. The market for fine tip markers is expected to grow steadily, driven by increasing use in art and design applications where intricate detailing is crucial. The versatility of fine tip markers also lends them well to use in office and educational settings, where they are used for writing and marking on a variety of surfaces.

Medium tip markers are perhaps the most versatile among the types, offering a balance between precision and coverage. These markers find applications across all major segments, including art and design, industrial use, and office supplies. The medium tip segment is expected to witness significant growth due to its adaptability and widespread acceptance in both professional and personal use. Their utility in creating bold, visible lines makes them a favorite for labeling and coding in industrial settings as well.

Broad tip markers, on the other hand, are predominantly used for applications requiring extensive coverage and visibility. Their thick, durable lines make them suitable for signage, posters, and industrial markings. The broad tip segment is projected to grow at a moderate rate, driven by consistent demand from industries that require clear and long-lasting markings on various surfaces. These markers are also popular in th

Recent studies have begun to elucidate the genetic basis for phenotypic traits in salmonid species, but many questions remain before these candidate genes can be directly incorporated into conservation management. In Chinook Salmon (Oncorhynchus tshawytscha), a region of major effect for migration timing has been discovered that harbors two adjacent candidate genes (greb1L, rock1), but there has been limited work to examine the association between these genes and migratory phenotypes at the individual, compared to the population, level. To provide a more thorough test of individual phenotypic association within lineages of Chinook Salmon, 33 candidate markers were developed across a 220 Kb region on chromosome 28 previously associated with migration timing. Candidate and neutral markers were genotyped in individuals from representative collections that exhibit phenotypic variation in timing of arrival to spawning grounds from each of three lineages of Chinook Salmon. Association tests confirmed the majority of markers on chromosome 28 were significantly associated with arrival timing and the strongest association was consistently observed for markers within the rock1 gene and the intergenic region between greb1L and rock1. Candidate markers alone explained a wide range of phenotypic variation for Lower Columbia and Interior ocean-type lineages (29% and 78%, respectively), but less for the Interior stream-type lineage (5%). Individuals that were heterozygous at markers within or upstream of rock1 had phenotypes that suggested a pattern of dominant inheritance for early arrival across populations. Finally, previously published fitness estimates from the Interior stream-type lineage enabled tests of association with arrival timing and two candidate markers, which revealed that fish with homozygous mature genotypes had slightly higher fitness than fish with premature genotypes, while heterozygous fish were intermediate. Overall, these results provide additional information for individual-level genetic variation associated with arrival timing that may assist with conservation management of this species.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database of sequence tagged sites (STSs) derived from STS-based maps and other experiments. STSs are defined by PCR primer pairs and are associated with additional information such as genomic position, genes, and sequences. Chromosome maps are labeled by name of the originating organism, the map title, total markers, total UniSTSs and links to view maps as well as research documents available through PubMed, another NCBI database. The search functions within UniSTS allow the user to search by gene marker, chromosome, gene symbol and gene description terms to locate markers on specified genes. A representation of the UniSTS datasets is available by ftp. NOTE: All data from this resource have been moved to the Probe database, http://www.ncbi.nlm.nih.gov/probe. You can retrieve all UniSTS records by searching the probe database using the search term unists(properties). (use brackets insead of parenthesis). Additionally, legacy data remain on the NCBI FTP Site in the UniSTS Repository (ftp://ftp.ncbi.nih.gov/pub/ProbeDB/legacy_unists).

In general, the mile markers are measured from west to east and south to north. Such as I-10 starting in Escambia County, bordering Alabama, with mile marker 1 then increases eastward. And for I-95 from Miami-Dade County starting with mile marker 1 then increases northward. The data is refreshed weekly and changes are made by the District Offices who are responsible to collect and upkeep the data. This data may also be collected for call boxes that are located at integral milepoints instead of the usual mile marker signs. This data is required for all interstate, tolled or non-tolled expressway facilities, and US routes. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 01/03/2026.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/milemarkers.zip