This dataset contains results presented in Nawaz, Urbanska, Herbig et al manuscript entitled ‘ Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting ’ available on bioRxiv (https://doi.org/10.1101/862227) and includes: (i) 29 '*.rtdc' files containing measurements performed with real time fluorescence and deformability cytometry 1,2 1 '*.fcs' file containing a measurement performed with a standard flow cytometer (BD LSR II, BD Biosciences) (iii) 1 '*.xlsx' file containing results from AFM-based indentation experiments. Sorting real-time fluorescence and deformability cytometry (soRT-FDC) is a robust sorting platform, combining image-based morphological cell analysis with mechanical characterisation and subsequent active sorting by feeding real-time fluorescence and deformability cytometry (RT-FDC) [2] information to a down-stream SSAW-based cell sorter [3]. Each filename of this dataset starts with the name of the figure to which the datafile corresponds. The measured samples are briefly described below. The 'Initial' measurements refer to the samples used for sorting, and the 'Target' measurements to the samples collected in the target outlet after sorting. For more detailed description of samples please refer to the manuscript. The '*.rtdc' files are HDF5 files and can be analysed using a Python library called dclab [4], or a software called Shape-Out [5]. They can also be opened using other HDF viewer programs. [1] Otto et al., "Real-time deformability cytometry: on-the-fly cell mechanical phenotyping". Nature Methods, 12(3):199–202, 2015. doi:10.1038/nmeth.3281. [2] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:10.1038/nmeth.4639. [3] Nawaz et al., “Acoustofluidic Fluorescence Activated Cell Sorter”. Analitycial Chemistry 87(24): 12051–12058, 2015. doi: doi.org/10.1021/acs.analchem.5b02398 [4] https://github.com/ZellMechanik-Dresden/dclab [5] https://github.com/ZellMechanik-Dresden/ShapeOut Figure1d_01_Beads_FL_Initial.rtdc, Figure1d_02_Beads_FL_Target.rtdc sample: 1:5 mixture of polyacrylamide microgel beads labeled with AlexaFluor488 and unlabeled ones, these beads were produced in house; sorted for fluorescence

The size of the Cell Sorting market was valued at USD XX Million in 2024 and is projected to reach USD XXX Million by 2033, with an expected CAGR of XX% during the forecast period.Separation of cells is the process of segregating the cells from each other on the basis of their physical or biological characteristics. Such a process of separation would involve the analysis of individual cells as they pass through the laser beam, then sorting them into different containers based on the specified characteristics like size, shape, and the presence of certain proteins. Cell sorting is applied in a range of research and clinical applications that include cancer research, immunology, and stem cell biology. This can help the scientists isolate different cell populations for further study in order to make the understanding of cellular function and the mechanisms behind disease more profound.

Cell Sorting Counter Market Outlook

The global cell sorting counter market size was valued at approximately $250 million in 2023 and is expected to reach nearly $600 million by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This significant growth can be attributed to advancements in cell sorting technologies, increasing investments in biotechnology research, and the rising prevalence of chronic diseases requiring advanced diagnostic and therapeutic approaches.

One of the primary growth factors driving the cell sorting counter market is the increasing demand for precision medicine. Precision medicine relies heavily on cell sorting technologies for personalized treatment plans, particularly in the fields of oncology and immunology. As healthcare providers strive to deliver targeted therapies, the need for accurate and efficient cell sorting methods has surged. The growing investments in research and development by both governmental and private entities are further propelling the market's expansion.

Another crucial factor contributing to the market's growth is the rapidly advancing technological landscape. Innovations such as microfluidics and magnetic-activated cell sorting (MACS) have revolutionized the cell sorting process, making it more efficient and less time-consuming. These technological advancements have not only improved the accuracy and throughput of cell sorting but have also reduced the associated costs, making these technologies more accessible to a broader range of end-users, including smaller research laboratories and academic institutions.

The increasing prevalence of chronic diseases such as cancer, diabetes, and autoimmune disorders is also a significant driver for the cell sorting counter market. These diseases necessitate sophisticated diagnostic and therapeutic techniques, wherein cell sorting plays a critical role. For instance, in cancer research, cell sorting is essential for isolating and analyzing specific cell populations, which can lead to more effective treatments and better patient outcomes. As the incidence of these diseases continues to rise globally, the demand for advanced cell sorting technologies is expected to grow correspondingly.

On a regional level, North America currently holds the largest share of the cell sorting counter market, driven by substantial investments in healthcare infrastructure and research initiatives. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, attributed to increasing healthcare expenditures, growing biotechnology sector, and rising awareness about advanced diagnostic techniques. Europe also represents a significant market segment, with a strong focus on research and innovation in life sciences.

Technology Analysis

The cell sorting counter market by technology encompasses several key methods, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), microfluidics, and other emerging technologies. Each of these technologies has its unique advantages and application areas, making them indispensable tools in biomedical research and clinical diagnostics. Fluorescence-activated cell sorting (FACS), for instance, is widely used due to its high precision and ability to sort a large number of cells rapidly.

Fluorescence-activated cell sorting (FACS) remains the gold standard in cell sorting technologies. It utilizes fluorescent markers to identify and sort cells based on their specific characteristics. The high specificity and throughput of FACS make it ideal for applications requiring detailed cell population analysis, such as immunology and oncology research. Recent advancements in FACS technology have further enhanced its capabilities, allowing for the simultaneous sorting of multiple cell types and improving overall efficiency.

Magnetic-activated cell sorting (MACS) is another widely adopted technology, particularly valued for its simplicity and cost-effectiveness. MACS uses magnetic beads coated with antibodies to selectively bind target cells, which are then separated using a magnetic field. This technology is especially useful in clinical settings where rapid and efficient cell sorting is required, such as in stem cell research and regenerative medicine. The growing adoption of MACS in various therapeutic applications is a testament to its efficacy and reliability.

Microfluidics is an emerging technology in the cell sorting counter market that offers several advantages over traditional

Counts of DAPI+ cells (white blood cells) and DAPI− cells (red blood cells and platelets) from three replicates of the blood cell sorting experiment in Fig 5.

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.

Novel Cell Sorting and Separation Market Outlook

The global novel cell sorting and separation market size was valued at approximately USD 3.2 billion in 2023 and is projected to reach around USD 7.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.4% during the forecast period. This significant growth is primarily driven by advancements in cell sorting technologies, increasing investments in healthcare and biotechnological research, and the rising prevalence of chronic diseases necessitating advanced diagnostic and therapeutic solutions.

One of the critical growth factors of the novel cell sorting and separation market is the rapid technological advancements in the field. Modern techniques such as fluorescence-activated cell sorting (FACS) and magnetic-activated cell sorting (MACS) have revolutionized the way cells are separated and analyzed. These methods offer high precision and efficiency, allowing researchers to sort cells based on specific markers swiftly. The advent of microfluidic technologies further enhances the ability to sort cells at a micro-scale, which is crucial for applications requiring high throughput and precision. The ongoing research and development in this domain promise even more advanced and efficient sorting techniques in the near future.

The increasing prevalence of chronic and infectious diseases is another primary driver for the market. Diseases such as cancer, HIV, and various autoimmune disorders require precise cell analysis for effective treatment. Novel cell sorting and separation technologies play a pivotal role in diagnosing these diseases at an early stage, thereby improving the prognosis. Additionally, these technologies are essential in the development of personalized medicine, which tailors treatment based on individual genetic makeup. As the demand for personalized therapy escalates, the need for advanced cell sorting techniques is expected to witness a substantial surge.

Investment in healthcare and biotechnological research has seen a considerable increase in recent years, further fueling market growth. Governments and private entities worldwide are allocating significant funds towards the development of advanced diagnostic and therapeutic solutions. This financial support has led to the establishment of numerous research institutes and laboratories equipped with state-of-the-art cell sorting technologies. Moreover, collaborations between academic institutions and industry players are fostering innovation, driving the market forward.

Regionally, North America currently dominates the novel cell sorting and separation market, attributed to its advanced healthcare infrastructure, significant research funding, and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as increasing healthcare expenditure, growing biotechnological research activities, and rising awareness about advanced diagnostic techniques are contributing to this growth. Countries like China, India, and Japan are emerging as significant contributors to the market, providing robust opportunities for market expansion.

Technology Analysis

Fluorescence-Activated Cell Sorting (FACS) Analysis

Fluorescence-Activated Cell Sorting (FACS) is a specialized type of flow cytometry that enables the sorting of a mixture of cells into different populations based on fluorescent labeling. This technology utilizes laser beams to excite fluorescently labeled antibodies on cell surfaces, allowing for the identification and separation of cells with high precision. The efficiency and accuracy of FACS make it a preferred choice for numerous research and clinical applications. The technique is particularly valuable for sorting rare cell populations, such as stem cells or cancer cells, which are critical for advanced research and therapeutic interventions.

The growth of FACS technology is driven by continuous innovations that enhance sorting speed and accuracy. Modern FACS machines are equipped with multiple lasers and detectors, enabling the analysis of several parameters simultaneously. This multi-parametric analysis allows researchers to gain deeper insights into cell biology and disease mechanisms. Additionally, the integration of artificial intelligence and machine learning algorithms in FACS systems is further improving their functionality, making cell sorting more efficient and less time-consuming.

Another significant factor contributing to the growth of FACS technology

In a survey conducted in August 2020 in Japan, **** percent of respondents stated that they used smartphones when ordering food delivery. The majority of food delivery orders were issued from smartphones, either through the web presence of the shop or dedicated applications.

Cell-cell interactions are important to numerous biological systems, including tissue microenvironments, the immune system, and cancer. However, current methods for studying cell combinations and interactions are limited in scalability, allowing just hundreds to thousands of multi-cell assays per experiment; this limited throughput makes it difficult to characterize interactions at biologically relevant scales. Here, we describe a new paradigm in cell interaction profiling that allows accurate grouping of cells and characterization of their interactions for tens to hundreds of thousands of combinations. Our approach leverages high throughput droplet microfluidics to construct multicellular combinations in a deterministic process that allows inclusion of programmed reagent mixtures and beads. The combination droplets are compatible with common manipulation and measurement techniques, including imaging, barcode-based genomics, and sorting. We demonstrate the approach by using it to enrich for CAR-T cells that activate upon incubation with target cells, a bottleneck in the therapeutic T cell engineering pipeline. The speed and control of our approach should enable valuable cell interaction studies. Comparison of sorted and unsorted CAR-T cells paired in droplets with Raji cells

Single cell RNA and TCR sequencing for sorted CD161int and CD161neg T cells. Cell ranger output files.

See Appendix C.

Multi Order Hydrologic Position (MOHP) raster datasets: Distance from Stream to Divide (DSD) and Lateral Position (LP) have been produced nationally for the 48 contiguous United States at a 30-meter resolution for stream orders 1 through 9.
These data are available for testing as predictor variables for various regional and national groundwater-flow and groundwater-quality statistical models.

The concept behind MOHP is that for any given point on the earth’s surface there is the potential for longer and longer groundwater flow paths as one goes deeper and deeper beneath the land surface. These increasing depths correspond to increasing stream orders. Or in other words, with increasing depth these paths of groundwater flow travel further from divides to point of discharge which are to increasingly larger streams of higher stream order.

DSD – Raster – Distance from Stream to Divide (DSD) rasters have cell values equal to the sum of the shortest distance to the stream or associated waterbody plus the shortest distance to the matching Thiessen divide. There are 9 rasters for streams orders 1 through 9. Units are in meters.

LP – Raster -- the lateral position (LP) raster has cell values equal to the shortest distance to the stream or associated waterbody divided by the DSD. There are 9 rasters for streams orders 1 through 9.

Combined, these two factors, DSD and LP, provide a measure or description of potential distance of groundwater flow to any location along the groundwater flow path.

174 views (1 recent) Dataset extent Map data © OpenStreetMap contributors. What is mobile sorting? It is a trailer fitted out and towed by a vehicle. Once on the site, the trailer unfolds to allow the public to be received and a ramp gives access to a sorting platform. This system allows residents to deposit their small bulky items in the specially designed boxes and crates adapted for the different types of waste, in order to encourage sorting and recycling.

This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows: matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz) *The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include: nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

DAERA commissioned Ulster University to undertake a Historical Shoreline Analysis project of the Northern Ireland coastline.A key requirement of the Historical Shoreline Analysis Project was to delineate first order coastal cell boundaries for the entire Northern Ireland coastline. Coastal cells are paramount for coastal managers to calculate the sediment budget on a specific coastal sector and identify zones within which changes to the coast might affect adjacent coastal areas.The boundary of each coastal cell is identified and mapped according to two basic types: littoral drift divides and sediment sinks.Littoral drift divides – These boundary types normally occur at a point where the coastal orientation changes abruptly (for example, at a headland), or where sediment drift occurs in opposing directions.Sediment sinks – These boundary types are the end points where sand or gravel transport routes terminate or meet. These can occur at deeply indented bays, tidal inlets, and estuaries. Sediment tends to accumulate in such locations forming beaches and/or sedimentary landforms at or close to the shore.The boundaries identified in the Historical Shoreline Analysis Project were delineated using the historical Ordnance Survey maps and the aerial photographs as well as expert coastal geomorphological knowledge. The end result is a spatial tool which will be extremely useful for future coastal management.This is the output, which divides the Northern Ireland coastline into 7 first order coastal cells.

We will be using G&T method to sequence single cell genome and transcriptome derived from FS13B iPSCs cell line. The cell cycle state of each of the single cells is known. Hence, we will be analysing the genome and transcriptome of single cells from each of the cell cycle state to generate a copy number profile and transcriptome profile per given cell cycle stage: G1, S, G2, S. . This dataset contains all the data available for this study on 2020-01-29.

Yearly citation counts for the publication titled "Suicide by order: Some open questions about the cell-killing activities of the TNF ligand and receptor families".

In 2021, the Chinese e-commerce platform Pinduoduo (PDD) received ** billion orders on its mobile shopping platform, increasing from **** billion in the previous year. Founded in 2015, Pinduoduo has become the second-largest online shopping platform in China based on the number of users.

Compact Automatic Cell Sorter Market Outlook

The Compact Automatic Cell Sorter market size is projected to grow significantly from $1.8 billion in 2023 to $3.7 billion by 2032, registering a robust CAGR of 8.6% during the forecast period. This substantial growth is primarily driven by the increasing demand for advanced cell sorting technologies across various sectors such as research, clinical applications, and industrial processes. The continuous advancements in biotechnology and medical research, coupled with the growing need for precision medicine, have fueled the adoption of compact automatic cell sorters, thereby propelling market expansion.

One major growth factor contributing to the booming market of compact automatic cell sorters is the surge in biotechnology research and development activities. As the biotechnology sector strives to achieve breakthroughs in disease treatment and personalized medicine, there is an escalating need for efficient and precise cell sorting technologies. Compact automatic cell sorters offer the ability to rapidly and accurately sort cells, which is crucial for research involving stem cells, cancer cells, and other specialized cell types. The miniaturization and automation of these devices enhance their usability in laboratory settings, facilitating a higher throughput of samples and providing researchers with comprehensive data to drive innovation.

Another key driver of market growth is the increasing prevalence of chronic and infectious diseases, which necessitates advanced diagnostic and therapeutic solutions. Compact automatic cell sorters play a pivotal role in clinical diagnostics by enabling the precise identification and isolation of specific cell populations. This capability is vital for the development of targeted therapies, particularly in oncology and immunology, where understanding cellular heterogeneity is essential for effective treatment strategies. Moreover, the use of cell sorting technology in clinical testing laboratories contributes to early and accurate diagnosis, ultimately improving patient outcomes and fueling the demand for these devices.

The industrial application of compact automatic cell sorters is another significant factor boosting market growth. In the industrial sector, particularly in pharmaceuticals and biotechnology manufacturing, the need for high-throughput cell sorting is critical for quality control and process optimization. These devices enable the purification and sorting of cells used in the production of biologics and vaccines, ensuring that only the highest quality cells are utilized. The automation and precision of compact cell sorters improve production efficiency and reduce costs, making them indispensable tools in large-scale manufacturing environments.

The integration of Microfluidic Cell Sorter technology into compact automatic cell sorters represents a significant advancement in the field of cell sorting. Microfluidic cell sorters utilize micro-scale channels to manipulate and sort cells with high precision and efficiency. This technology offers several advantages, including reduced sample volume requirements, faster processing times, and enhanced accuracy in cell sorting. By incorporating microfluidic techniques, compact automatic cell sorters can achieve higher throughput and better resolution, making them ideal for complex biological applications. The ability to precisely control fluid dynamics at the microscale level allows for the gentle handling of cells, minimizing stress and preserving cell viability, which is crucial for downstream applications in research and clinical settings.

Regionally, North America holds a dominant position in the compact automatic cell sorter market due to the presence of a well-established biotechnology industry, significant investments in research and development, and a favorable regulatory environment. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by increasing healthcare expenditures, growing biotechnology initiatives, and expanding research infrastructure in countries like China and India. The region's burgeoning pharmaceutical industry and rising focus on improving healthcare outcomes further contribute to the market's upward trajectory.

Product Type Analysis

In the realm of compact automatic cell sorters, product type plays a crucial role in defining market dynamics, with benchtop and floor-standing models being the primary categories. Benc

Summary

Dataset of confocal microscopy data of cells exposed to gamma-irradiation and immunostained with gH2AX and 53BP1.

Nuclei segmentation Head and neck primocultures immunostained with gH2AX/53BP1, Training/Testing/Validation dataset (nucleus_segmentation.zip) (available in part 1, https://dx.doi.org/10.5281/zenodo.4067741)

IRIF Foci: Head and neck primocultures immunostained with gH2AX/53BP1, Training/Testing/Validation dataset. (foci_detection.zip) (available in part 1, https://dx.doi.org/10.5281/zenodo.4067741)

Cell lines: U-87 and NHDF Cells exposed to 0.5-8 Gy 30 min and 8h post irradiation - confocal microscopy data of gH2AX/53BP1/DAPI annotated for gH2AX, 53BP1 and colocalized foci separately (cell_lines_U87.zip and cell_lines_NHDF.zip, this part of dataset)

Code: the code is available at https://github.com/tomasvicar/DeepFoci

Preprint: Vicar et al, DeepFoci: Deep Learning-Based Algorithm for Fast Automatic Analysis of DNA Double Strand Break Ionizing Radiation-Induced Foci, 10.1101/2020.10.07.321927

Publication: Vicar et al, TBA

Materials and methods

Dataset

Following cells were used:

1) The training/validation/testing datasets was based on patient-derived primary cell cultures prepared from spinocellular tumors and morphologically normal tissues adjacent to the tumor taken from patients suffering from head and neck cancer. The dataset was divided into two subsets: one for training, validation and testing the nucleus segmentation (237/10/30 fields of view (FOVs), respectively) and one for training, validation and testing the focus segmentation (239/60/100 FOVs). The dataset consisted of several cell types: a) tumor cells, b) tumor-associated fibroblasts, and c) cells from morphologically normal tissues. All cell types were fixed at different periods of time (0 (non-irradiated control), 0.5, 8 or 24 h PI) after exposure to 2 Gy of gamma-rays. The representation of cells in two subsets with respect to the cell type and post-irradiation time (i.e., DSB repair duration) was random.

2) The evaluation dataset was used to assess the robustness of segmentation procedures. It was composed of multiple types of differently treated cells in order to represent a highly challenging dataset maximally reflecting high biological and technical variability between samples, as it may appear in research or clinical practice. The dataset contained a) mesenchymal NHDF fibroblasts coming from a standard permanent cell line, b) radioresistant U-87 glioblastoma cells coming from a standard permanent cell line, c) tumor cells (CD90-) and tumor-associated fibroblasts (CD90+) prepared as a primary culture from a spinocellular tumors of patients (different from dataset 1) suffering from a head and neck cancer, and d) cells prepared as primary cultures from morphologically normal tissue adjacent to tumors of involved head and neck cancer patients. NHDF and U-87 cells received 0.5, 0, 1, 2, 4 and 8 Gy of gamma-rays and were fixed at 30 min and 8 h post-irradiation, while the primary cultures were only exposed to the dose of 2 Gy (for a limited amount of the cell material) and fixed at 0 (non-irradiated control), 0.5, 8 or 24 h post-irradiation times.

Gamma irradiation

The cells were irradiated at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic in a following schemes: (a) patient-derived primoculture was irradiated with a single dose of 2 Gy (D = 1 Gy/min) of gamma-rays (60Co, Chisostat, Chirana, CR) , (b) U-87 and NHDF cells were irradiated with doses 0.5-8 Gy (D = 1 Gy/min). Cells were irradiated in RPMI 1640 medium (37 °C, normal atmosphere). Confocal microscopy of gammaH2AX and 53BP1 foci immunodetection was consequently performed.

Fluorescent staining

DNA double strand breaks (DSBs) were quantified in different periods of time post-irradiation (30 min, 8h and 24h post irradiation) by means of $\gamma$H2AX and 53BP1 foci immunodetection combined with confocal microscopy. For details see \cite{falk2007chromatin}.

Confocal microscopy

The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.

File description

all files are compressed hyperstack tiffs (50 Z slices and 3 fluorescent channels, XYCZ order), 100x magnification

foci_detection.zip: IRIF Foci (available in part 1, https://dx.doi.org/10.5281/zenodo.4067741): Head and neck primocultures immunostained with gH2AX/53BP1, Training/Testing/Validation dataset: FOVs 240/100/120 files for training, testing, and validation, organisation:

data_001.tif – 3channel Z.stack tiff

mask_001.tif – respective Z stack mask with single points per IRIF focus (manual annotation, training and testing subsets only)

data_description.xlsx – description of sample type (2Gy post irradiation times and characteristics of primary culture of squamous cell cancer of patients)

data_001_pos.csv – manual annotation of gH2AX/53BPI IRIF foci by two experts – cordinates file (in 2D, validation only)

nucleus_segmentation.zip (available in part 1, https://dx.doi.org/10.5281/zenodo.4067741) Nuclei segmentation Head and neck primocultures immunostained with gH2AX/53BP1, FOVs 237 Training/ 30 Testing/ 10 Validation dataset

data_001.tif – 3channel Z.stack tiff

mask_001.tif – respective Z stack mask with manually annotated nucleus mask (manual annotation, training and testing subsets only)

data_description.xlsx – description of sample type (2Gy post irradiation times and characteristics of primary culture of squamous cell cancer of patients)

U87.zip and NHDF.zip: Annotated gH2AX/53BP1 foci in cell lines exposed to increasing dose, annotations performed for gH2AX, 53BP1 and colocalized focus separatelly. 679 annotated FOVs for both cell lines.

control.png - RGB control figure showing merge and annotated overlay

data_53BP1.tif - TIFF Z-stack, confocal microcopy, 53BP1 channel

data_DAPI.tif - TIFF Z-stack, confocal microcopy, DAPI channel

data_gH2AX.tif - TIFF Z-stack, confocal microcopy, gH2AX channel

labels.json - foci labels for individual channel

mask.tif - generated Z-stack of nucleus mask

Higher taxon and mean cell volume (Vm; µm3; grand mean across all treatments) of phytoplankton species, arranged in descending order of size.