The analysis code, the visualization of all correction results of benchmark methods, the preprocessed benchmark datasets in the paper "Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space"doi of our paper: https://doi.org/10.1002/advs.202306770

This RDS file contains processed single-cell RNA sequencing (scRNA-seq) data comparing immune cell populations from germ-free (GF) and specific-pathogen-free (SPF) mice. The dataset includes:Samples: Peripheral blood (PB) and bone marrow (BM) from GF and SPF miceCell Counts:Raw: 21,827 cells (PB) and 19,940 cells (BM)Quality-filtered: 18,344 high-quality cells (PB) and 16,537 high-quality cells (BM)Gene Coverage: Median 1,426 genes per cell (PB) and 1,391 genes per cell (BM)Cell Classifications: 18 major cell identities further divided into 25 subpopulationsAnnotation: Cells identified using established marker genes for blood cells

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver.

This dataset contains single cell RNA sequencing data of PBMC samples from 10 bladder cancer patients. cDNAs and single cell RNA libraries were prepared following manufacturer’s user guide (10x Genomics). Each library was sequenced in HiSeq4000 (Illumina) to achieve ~300 million reads following manufacturer’s sequencing specification.

10 single-cell placental RNA libraries were generated using the Chromium Single Cell 3′ Reagent Kit (10X Genomics). All single-cell libraries were sequenced with a customized paired end with dual indexing (98/14/8/10-bp) format according to the recommendation by 10X Genomics. The data were aligned using the Cell Ranger Single-Cell Software Suite (version 1.0). Moreover, plasma RNA from 22 samples were extracted using the RNeasy Mini Kit (Qiagen). cDNA reverse transcription, second-strand synthesis, and RNA-sequencing (RNA-seq) library construction were performed using the Ovation RNA-seq System V2 (NuGEN) kit according to the manufacturer’s protocol. For alignment of the plasma RNA library, adaptor sequences and low-quality bases on the fragment ends (i.e., quality score < 5) were trimmed, and reads were aligned to the human reference genome (hg19) using the TopHat (v2.0.4) software. All aligned reads were deposited in bam file format.

we collected 40 tumor and adjacent normal tissue samples from 19 pathologically diagnosed NSCLC patients (10 LUAD and 9 LUSC) during surgical resections, and rapidly digested the tissues to obtain single-cell suspensions and constructed the cDNA libraries of these samples within 24 hours using the protocol of 10X gennomic. These libraries were sequenced on the Illumina NovaSeq 6000 platform. Finally we obtained the raw gene expression matrices were generated using CellRanger (version 3.0.1). Information was processed in R (version 3.6.0) using the Seurat R package (version 2.3.4).

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NGS-Based Rna-Seq Market Size 2024-2028

The NGS-based RNA-seq market size is forecast to increase by USD 6.66 billion, at a CAGR of 20.52% between 2023 and 2028.

The market is witnessing significant growth, driven by the increased adoption of next-generation sequencing (NGS) methods for RNA-Seq analysis. The advanced capabilities of NGS techniques, such as high-throughput, cost-effectiveness, and improved accuracy, have made them the preferred choice for researchers and clinicians in various fields, including genomics, transcriptomics, and personalized medicine. However, the market faces challenges, primarily from the lack of clinical validation on direct-to-consumer genetic tests. As the use of NGS technology in consumer applications expands, ensuring the accuracy and reliability of results becomes crucial.
The absence of standardized protocols and regulatory oversight in this area poses a significant challenge to market growth and trust. Companies seeking to capitalize on market opportunities must focus on addressing these challenges through collaborations, partnerships, and investments in research and development to ensure the clinical validity and reliability of their NGS-based RNA-Seq offerings.

What will be the Size of the NGS-based RNA-Seq market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in NGS technology and its applications across various sectors. Spatial transcriptomics, a novel approach to studying gene expression in its spatial context, is gaining traction in disease research and precision medicine. Splice junction detection, a critical component of RNA-seq data analysis, enhances the accuracy of gene expression profiling and differential gene expression studies. Cloud computing plays a pivotal role in handling the massive amounts of data generated by NGS platforms, enabling real-time data analysis and storage. Enrichment analysis, gene ontology, and pathway analysis facilitate the interpretation of RNA-seq data, while data normalization and quality control ensure the reliability of results.

Precision medicine and personalized therapy are key applications of RNA-seq, with single-cell RNA-seq offering unprecedented insights into the complexities of gene expression at the single-cell level. Read alignment and variant calling are essential steps in RNA-seq data analysis, while bioinformatics pipelines and RNA-seq software streamline the process. NGS technology is revolutionizing drug discovery by enabling the identification of biomarkers and gene fusion detection in various diseases, including cancer and neurological disorders. RNA-seq is also finding applications in infectious diseases, microbiome analysis, environmental monitoring, agricultural genomics, and forensic science. Sequencing costs are decreasing, making RNA-seq more accessible to researchers and clinicians.

The ongoing development of sequencing platforms, library preparation, and sample preparation kits continues to drive innovation in the field. The dynamic nature of the market ensures that it remains a vibrant and evolving field, with ongoing research and development in areas such as data visualization, clinical trials, and sequencing depth.

How is this NGS-based RNA-Seq industry segmented?

The NGS-based RNA-seq industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Acamedic and research centers
  Clinical research
  Pharma companies
  Hospitals


Technology

  Sequencing by synthesis
  Ion semiconductor sequencing
  Single-molecule real-time sequencing
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Singapore


  Rest of World (ROW)

.

By End-user Insights

The acamedic and research centers segment is estimated to witness significant growth during the forecast period.

The global next-generation sequencing (NGS) market for RNA sequencing (RNA-Seq) is primarily driven by academic and research institutions, including those from universities, research institutes, government entities, biotechnology organizations, and pharmaceutical companies. These institutions utilize NGS technology for various research applications, such as whole-genome sequencing, epigenetics, and emerging fields like agrigenomics and animal research, to enhance crop yield and nutritional composition. NGS-based RNA-Seq plays a pivotal role in translational research, with significant investments from both private and public organizations fueling its growth. The technology is instrumental in disease research, enabling the identification

It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, timepoints and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, Cluster Similarity Spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals.

The presented data set here includes 1) the seurat object of the published two-month-old human cerebral organoid scRNA-seq data (Kanton et al. 2019 Nature); 2) the single-cell RNA-seq data of cerebral organoid generated by inDrop; 3) the newly generated single-cell RNA-seq data of cerebral organoids with and without fixation conditions.

Mapping the human body at the cellular level.

Results of scTCRseq and VIDJIL run on simulated scRNAseq data at different read lengths and coverage. Provided as a separate Excel file. (XLSX 10 kb)

Spatial Single-Nucleus RNA-Seq Market Outlook

According to our latest research, the global Spatial Single-Nucleus RNA-Seq market size reached USD 482.3 million in 2024, driven by a robust surge in demand for advanced transcriptomic profiling technologies across biomedical research and clinical diagnostics. The market is expected to expand at a CAGR of 16.9% over the forecast period, reaching approximately USD 1,543.7 million by 2033. This impressive growth trajectory is fueled by rapid technological advancements, increasing investments in genomics research, and the growing adoption of precision medicine initiatives worldwide.

The primary growth factor for the Spatial Single-Nucleus RNA-Seq market is the escalating need for high-resolution, single-cell transcriptomic analysis, especially in complex tissues where traditional single-cell RNA sequencing faces limitations. Spatial single-nucleus RNA sequencing enables researchers to dissect gene expression patterns at the nucleus level, preserving spatial context and allowing for a deeper understanding of cellular heterogeneity within tissues. This technology has become particularly invaluable in fields such as cancer research, neurology, and immunology, where understanding the spatial distribution and functional states of cells can drive breakthroughs in disease mechanisms and therapeutic development. The integration of spatial data with single-nucleus sequencing is revolutionizing the landscape of molecular biology, propelling market growth as institutions and companies invest heavily in cutting-edge research tools.

Another significant driver is the influx of funding from governmental bodies, private investors, and large pharmaceutical companies aimed at accelerating the deployment of next-generation sequencing technologies. The increasing prevalence of complex diseases, such as cancer and neurodegenerative disorders, is prompting a shift toward more sophisticated molecular profiling techniques. Spatial single-nucleus RNA-Seq offers unparalleled insights into cellular architecture and gene regulation, which are critical for identifying novel drug targets and biomarkers. Furthermore, the growing emphasis on personalized medicine and the need for robust, reproducible data in clinical and translational research have encouraged the adoption of these advanced sequencing platforms in both academic and commercial settings.

The market is also benefiting from rapid advancements in sequencing platforms, bioinformatics tools, and automation technologies. The development of user-friendly and scalable instruments, coupled with high-throughput consumables and intuitive software solutions, is making spatial single-nucleus RNA-Seq more accessible to a broader range of end users. As the cost of sequencing continues to decline and the efficiency of analysis improves, more laboratories and research institutions are integrating these technologies into their workflows. Additionally, strategic collaborations between technology providers, research consortia, and healthcare organizations are fostering innovation and accelerating the translation of spatial transcriptomics into clinical applications, thus amplifying market expansion.

From a regional perspective, North America currently dominates the Spatial Single-Nucleus RNA-Seq market, accounting for the largest share, followed by Europe and Asia Pacific. The United States, in particular, boasts a mature genomics infrastructure, significant research funding, and a strong presence of leading technology providers. Europe is witnessing increased adoption due to growing research initiatives and supportive government policies, while Asia Pacific is emerging as a high-growth region, fueled by expanding biotechnology industries and increasing investments in healthcare innovation. Latin America and the Middle East & Africa are gradually catching up, with rising awareness and adoption of advanced molecular profiling techniques in academic and clinical settings.

Product Type Analysis

The Product Type&l

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Single Cell Analysis Market Size 2025-2029

The single cell analysis market size is forecast to increase by USD 4.63 billion at a CAGR of 18.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing prevalence of cancer and the rising incidence of chronic diseases and genetic disorders. This market is driven by the need for more precise and personalized diagnostic and therapeutic approaches, which single cell analysis provides. However, the high cost of single cell analysis products remains a major challenge for market expansion, limiting accessibility to this technology for many healthcare providers and research institutions. Despite this, the market's potential is vast, with opportunities in various end-user industries such as pharmaceuticals, biotechnology, and academia. This approach, which combines data from genomics, transcriptomics, proteomics, and metabolomics, among others, can provide valuable insights into cellular function and behavior.
Companies seeking to capitalize on this market's growth should focus on developing cost-effective solutions while maintaining the high-quality standards required for single cell analysis. Additionally, collaborations and partnerships with key opinion leaders and research institutions can help establish market presence and credibility. Overall, the market presents a compelling opportunity for companies to make a significant impact on the healthcare industry by enabling more accurate diagnoses and personalized treatments.

What will be the Size of the Single Cell Analysis Market during the forecast period?

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Single-cell analysis, a cutting-edge technology, is revolutionizing the healthcare industry by enabling a more comprehensive knowledge of complex biological systems. This advanced approach allows for the examination of individual cells, providing insights into clinical trial design, tumor microenvironment, and patient stratification. Technologies such as single-cell spatial transcriptomics, microfluidic chips, and droplet microfluidics facilitate the analysis of cell diameter, morphology, immune cell infiltration, and cell cycle phase. Furthermore, single-cell lineage tracing, immune profiling, developmental trajectory analysis, and spatial proteomics offer valuable information on circulating tumor cells and tumor heterogeneity. Single-cell analysis software, genome-wide association studies, and epigenetic analysis contribute to the interpretation of vast amounts of data generated.
Drug response prediction, cell interactions, and biomarker validation are additional applications of this technology. Single-cell analysis services and consulting firms facilitate the implementation of this technology in research and clinical settings. Protein expression profiling, encapsulation, and cell-free DNA analysis through liquid biopsy further expand the scope of single-cell analysis. This technology's potential is vast, offering significant advancements in diagnostics, therapeutics, and fundamental research.

How is this Single Cell Analysis Industry segmented?

The single cell analysis industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Consumables
  Instrument


Type

  Human cells
  Animal cells


Technique

  Flow cytometry
  Next-generation sequencing (NGS)
  Polymerase chain reaction (PCR)
  Microscopy
  Mass spectrometry


Application

  Research
  Medical


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea

By Product Insights

The consumables segment is estimated to witness significant growth during the forecast period. The market encompasses various technologies and applications, including cell stress analysis, omics data integration, cellular heterogeneity, cell engineering, single-cell immunophenotyping, single-cell DNA sequencing, cell proliferation assays, systems biology, precision medicine, cellular metabolism, single-cell proteomics, gene editing, imaging cytometry, academic research, mass cytometry, single-cell barcoding, single-cell spatial analysis, microarray analysis, single-cell sequencing, machine learning, biopharmaceutical industry, data visualization, next-generation sequencing, developmental biology, biotechnology industry, clinical diagnostics, cell cycle analysis, high-throughput screening, cell signaling, regenerative medicine, cell line development, cancer research, flow cytometry, drug discovery, stem cell research, cell culture, cell differentiation assays, biomarker discovery, personalized medicine, single-cell RNA sequencing, single-cell methylation analysis, single-cell data analysis, multiplexed analysi

This item is part of the Figshare Project: Early mitochondrial dysfunction revealed across FUS- and TARDBP-ALS at single cell resolution

From Data Availability Statement for the forthcoming paper in Nature Communications entitled:

Single-cell RNA sequencing reveals early mitochondrial dysfunction unique to motor neurons shared across FUS- and TARDBP-ALS

"We have deposited all raw and processed RNA sequencing data generated in this study on the NCBI Gene Expression Omnibus (GEO) under the accession number GSE226482. The C9orf72-ALS bulk RNA sequencing data was retrieved directly from the authors of the study.

[Items under this Figshare Project contain:] "Scans of fluorescent western blots, raw imaging files from confocal microscopy, the analysis files from Opera Phenix, qPCR data sets, and Seahorse assay result files."

[Item specific description:]

Expression of pluripotency markers in genome-edited iPSC lines by immunofluorescene microscopy.

The microscopy file format is ND2 which can be opened with the free standalone program NIS-Elements Viewer (Nikon). More information about the software and file format can be found here: https://www.microscope.healthcare.nikon.com/products/software/nis-elements/software-resources

Here we employed single cell RNA sequencing to identify the transcriptional program of Nanos and Vasa positive cells and their changes during development. Our single cell sequencing analysis of six developmental stages in P. miniata revealed cell types derived from the three germ layers and expression of the germ cell genes Nanos and Vasa. We used these datasets to parse out 20 cell lineages of the embryo identified by this approach and to focus on the key transitions of germ cell gene expression and test their coexpression with key signaling components. Overall design: Adult Patiria miniata animals were collected by either Peter Halmay (PeterHalmay@gmail.com) or Josh Ross (info@scbiomarine.com) off the Californian coast. Embryos were cultured essentially as described previously (Fresques et al., 2016). Embryos were cultured in filtered (0.2micron) sea water collected at the Marine Biological laboratories in Woods Hole MA, until the appropriate stage for dissociation. All embryos used in the study resulted from mating of one male and one female. Multiple fertilizations were initiated in this study and timed such that the appropriate stages of embryonic development were reached at a common endpoint. The embryos were then collected and washed twice with calcium-free sea water, and then suspended hyalin-extraction media (HEM) for 10-15 minutes, depending on the stage of dissociation. When cells were beginning to dissociate, the embryos were collected and washed in 0.5M NaCl, gently sheared with a pipette, run through a 40micron Nitex mesh, counted on a hemocytometer, and diluted to reach the appropriate concentration for the scRNA-seq protocol. Equal numbers of embryos were used in each time point and at no time were cells or embryos pelleted in a centrifuge (Oulhen et al., 2019).

This repository provides the processed data necessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer Funnell, O’Flanagan, Williams et al" This includes the following: Single cell whole genome sequencing Allele specific copy number profiles SNV counts per cell Structural variant counts per cell QC metrics clone assignments phylogenetic trees computed with sitka benchmarking results vs other methods bulk whole genome sequencing copy number profiles SNVs 10X single cell RNA sequencing count matrices seurat Rdata objects analysis tables downstream processed results used to generate figures oxford nanopore phasing results For further information please feel free to get in touch with Marc Williams (william1 [at] mskcc.org)

This item is part of the Figshare Project: Early mitochondrial dysfunction revealed across FUS- and TARDBP-ALS at single cell resolution

From Data Availability Statement for the forthcoming paper in Nature Communications entitled:

Single-cell RNA sequencing reveals early mitochondrial dysfunction unique to motor neurons shared across FUS- and TARDBP-ALS

"We have deposited all raw and processed RNA sequencing data generated in this study on the NCBI Gene Expression Omnibus (GEO) under the accession number GSE226482. The C9orf72-ALS bulk RNA sequencing data was retrieved directly from the authors of the study.

[Items under this Figshare Project contain:] "Scans of fluorescent western blots, raw imaging files from confocal microscopy, the analysis files from Opera Phenix, qPCR data sets, and Seahorse assay result files."

[Item specific description:]

FUS localization in genome-edited iPSC lines

These files are immunofluorescent stainings of FUS localization in genome-edited iPSC-lines

The microscopy file format is ND2 which can be opened with the free standalone program the NIS-Elements Viewer (Nikon). More information about the software and file format can be found here: https://www.microscope.healthcare.nikon.com/products/software/nis-elements/software-resources

Our study investigated the heterogeneity in glucose-induced excitability within islet cells, providing a cellular basis for their roles in glucose-responsive hormone secretion. To comprehensively map glucose-induced excitability profiles, we developed SHIMMER, a high-throughput, multimodal data acquisition platform that integrates electrical signals, Ca2+ fluxes, and single-cell sequencing data. By quantifying the non-stationary electrical and calcium signal dynamics, we formulated a machine learning-based electro-calcium model that shows how variations in membrane conductance and Ca2+ clearance contribute to increased heterogeneity in cytosolic Ca2+ accumulation. Using SHIMMER for single-cell sequencing on representative cells, we identified several genes that associated with electro-calcium characteristics, and found a potential marker for a highly excitable cell subpopulation. These findings indicate that islet cells tend to exhibit a large heterogeneity in glucose-induced excitability, being essential for their normal functioning.

This dataset include all processed data we mentioned in our paper, the code were uploaded on https://github.com/CullinanLi/heterogeneity-in-pancreatic-islet-cells, with the same folder name, if we missed anything, please feel free to contact the lead contact.

Plants respond to environmental stresses through controlled stem cell maintenance and meristem activity. One level of transcriptional control is RNA alternative splicing. However the mechanistic link between stress, meristem function and RNA splicing is poorly understood. The MERISTEM-DEFECTIVE (MDF)/DEFECTIVELY ORGANIZED TRIBUTARIES (DOT2) gene of Arabidopsis encodes a SR-related family protein, required for meristem function and leaf vascularization, and is the likely orthologue of the human SART1 and yeast snu66 splicing factors. MDF is required for the correct splicing and expression of key transcripts associated with root meristem function. We identified RSZ33 and ACC1, both known to regulate cell patterning, as splicing targets required for MDF function in the meristem. MDF expression is modulated by osmotic and cold stress, associated with differential splicing and specific isoform accumulation and shuttling between nucleus and cytosol, and acts in part via a splicing target SR34. We propose a model in which MDF controls splicing in the root meristem to promote stemness and repress stress response and cell differentiation pathways. Methods RNA extraction and sequencing RNA was extracted from three independent biological replicates using 7-day-old seedlings (ca. 100 mg tissue) grown on half-strength MS10 medium using the Sigma-Aldrich Plant Total RNA Kit (catalog number STRN50), with the On-Column DNase I Digestion Set (catalog Number: DNASE10-1SET) to eliminate any residual DNA molecules. Plant tissue was ground in liquid nitrogen before incubation in a lysis solution containing 2-mercaptoethanol at 65°C for 3 minutes. The solid debris was removed by centrifuging and 14 000 x g and column filtration before RNA was captured onto a binding column using the supplied binding solution, which helps preventing polysaccharide and genomic DNA from clogging the column. Most DNA was removed by wash solutions, and any trace of residual DNA was removed by DNase on the column. Then purified RNA was eluted using RNAase-free water. RNA sequencing from three biological replicate samples was carried out on an Illumina HiSeq 2500 System with the library prepared using the Illumina TruSeq Stranded Total RNA with Ribo-Zero Plant Sample Preparation kit (catalog Number: RS-122-2401). Ribosomal RNA (rRNA) was removed from isolated total RNA using biotinylated, target-specific oligos on rRNA removal beads. Purified RNA was quality checked using a TapeStation 2200 (Agilent Technology) with High Sensitivity RNA ScreenTape (catalog Number: 5067-5579), and the mRNA was fragmented into 120-200 bp sequences with a median size of 150 bp. Fragmented mRNA was used as a template to synthesise first-strand cDNA using reverse transcriptase and random primers, followed by second-strand cDNA synthesis with DNA Polymerase I and RNase H. Newly synthesised cDNA had a single adenine base added with ligation of adaptors, before being purified and amplified by PCR to make the final library. Library quality control was performed again using a TapeStation with D1000 ScreenTape (catalog Number: 5067-5582). Pre-processing of RNA-seq data, differential expression and differential usage analysis. RNAseq data were processed and aligned against the TAIR10 (EnsemblePlants) genome using TopHat and indexed with Samtools. DeSeq determined differential expression. Alternative splicing analysis was determined using RMats (p value of 0.05, a minimum of 10% inclusion difference). Alternative splicing events were visualised using Sashimi plots generated by the Integrative Genomics Viewer (IGV) (Robinson et al., 2011). Direct mRNA isolation and cDNA preparation for RT-qPCR or RT-PCR Seedlings were grown 7 days post-germination as described above. Roots and cotyledons were separated using a razor blade, and the material was frozen immediately in liquid nitrogen. Pools of seedlings were used to generate three separate biological samples. Each pool contained approximately 20 mg of root or cotyledon tissue. Total mRNA was extracted using Dynabeads®mRNA DIRECT™kit with Oligo(dT)25 labelled magnetic beads. Frozen tissue was ground with a sterile plastic micropestle and resuspended in 300 µl lysis buffer. The solution was then forced through a 21-gauge needle in a 1ml syringe 3-5 times to shear any DNA and mixed with 50 µl of Dynabeads Oligo(dT)25. The kit procedure was followed, with two final washes conducted. To ensure the complete removal of any genomic DNA in the subcellular fractionation experiments, this stage was followed by ezDnase™ treatment in a 10 µl volume (1µl ezDNASe™, 1 µl ezDNASe™ 10X buffer and 8 µl sterile H2O), 37˚C for 2 minutes followed by 1 µl DTT and 5 minutes at 55˚C in a heat block. cDNA was prepared using a SuperScript®IV First-Strand synthesis system directly on the bead solution. For RT-PCR and RT-qPCR beads were washed in 20 µl 1 x SSIV buffer before resuspension in 12 µl sterile H2O with 1 µl dNTP 10 mM each mix and incubated for 5 minutes at 50 ˚C in a Proflex PCR machine (Applied Biosystems). Then the following were added 4 µl 10 x SSIV buffer, 1 µl ribonuclease inhibitor and 1 µl Superscript®IV reverse transcriptase were added. The mixture was mixed by pipetting and incubated for 10 minutes at 50 ˚C, followed by 10 minutes at 80 ˚C, and then held at 4 ˚C. The 20 µl cDNA mix was stored at -20 and not eluted from the beads. Samples were checked for the presence of genomic DNA by PCR with Actin 2 primers ACT2 forward and reverse. A PCR reaction after 28 cycles with a Tm of 60 ˚C generated a 340 bp product if genomic DNA was a contaminant, 240 bp otherwise. All PCR and sequencing primers are listed in Table S3. RT-PCR 0.5 – 1 µl of cDNA/bead mix were used per PCR reaction. RT-PCR was performed with RSZ33 or ACC1 root-derived cDNA using Phusion™ (Thermofisher) high-fidelity polymerase. Relative levels of RSZ33 and ACC1 splice variants were determined using FIJI gel analysis software (Schindelin et al. 2012). Relative levels of cDNA per sample were determined using PCR-amplified PP2a transcript levels.

Patient-derived organoids (PDOs) offer new opportunities to model various cancers. However, their application in prostate cancer (PCa) has been hampered by poor take-rates and overgrowth of benign cell types. Initial experiments with 136 samples highlighted the limitations of existing culture conditions, while 30 additional samples were subsequently used to explore the effects of niche factors, carbon source, and extracellular matrix (ECM) composition on organoid outcome. Single-cell RNA sequencing (scRNA-seq) reveals that Matrigel-free PDOs exhibit cellular heterogeneity and preserve patient-specific PCa cell populations with active AR signaling, while enriching in intermediate club cell populations. In contrast, Matrigel fails to maintain primary PCa cells and produces in vitro basal-like benign transcriptomic profiles that are divergent from patient samples. Furthermore, we redefine cell type-signatures, identifying RNA- and protein-based biomarkers discriminating tumor versus all other cell types ex vivo, and show that expression of laminin-binding integrins is a hallmark of Matrigel-derived organoids. Finally, integrating previously-published datasets with our new data, we generate the first Prostate PDO single-cell atlas (PPScA). The PPScA captures a spectrum of cellular identities and malignancies, while revealing pathways universally altered in PDOs as compared to primary PCa tissues. Altogether, our study represents a significant advancement in the field, providing methodological improvements and novel cellular biology insights.

To improve our understanding of MS pathology and heterogeneity and provide new strategies for biomarkers and treatments development we carried out a genome-wide small non-coding RNA analysis. The analysis was performed in paired peripheral blood mononuclear cells, plasma, cerebrospinal fluid (CSF) cells and cell-free CSF from 29 MS patients and 16 controls using next-generation sequencing. We aimed to detect differentially expressed small non-coding RNAs between MS and controls. The data contains unique molecular identifier (UMI) count information for each transcript.

The small non-coding RNA analysis was performed in paired peripheral blood mononuclear cells, plasma, cerebrospinal fluid (CSF) cells and cell-free CSF from 29 MS patients and 16 controls using next-generation sequencing. One INDC control was missing a CSF cell and cell-free CSF sample and one NINDC control was missing a PBMC and plasma sample. Small non-coding RNAs were isolated from 300 ul of plasma or CSF using the miRCURY RNA isolation kit for biofluids (Exiqon, Denmark) or from CSF cells and peripheral blood mononuclear cells precipitate using the miRNAeasy micro kit (Qiagen, Germany). Small non-coding RNA libraries were prepared as previously described at PMID: 27798564. The libraries were sequenced on eight lanes of HiSeq2500. Preprocessing and alignment were done according to PMID: 30250291.

The "Unique molecular identifier_sncRNAs_analysis_MS" file contains unique molecular identifier count information for each small non-coding RNA transcript as well as other types of transcripts identified in the sequencing libraries from 44 individuals and 4 compartments (PBMCs, CSF cells, plasma, cell-free CSF). Altogether 176 samples.

The metafile contains information about the disease status, sex, age for all MS patients and controls. The "readme" explains the contents of the data files and contains the variables list.