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Forward-scatter and side-scatter dataset for epithelial cells from touch samples analyzed by flow cytometry.

ABSTRACT: 'Touch' or trace biological samples submitted to caseworking labs as evidence often contain biological material from multiple individuals which can result in mixed DNA profiles. These mixture profiles are difficult to interpret and may cause analytical bottlenecks for forensic laboratories. The data in this brief reports the variation in the relative abundance of intact epithelial cells deposited by four different donors across nine days. Touch samples were generated each day by rubbing a polypropylene tube with both hands for five minutes. Forward-scatter area (FSC-A) and side-scatter area (SSC-A) data was acquired with the BD FACSCanto™ II Analyzer. The relative abundance of different sub-populations within the FSC-A and SSC-A plots was calculated against the total number of events analyzed in each sample. Mean and standard deviation values were calculated for each donor.

SUBMITTER: Stanciu CE

PROVIDER: S-EPMC4709467 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Forward-scatter and side-scatter dataset for epithelial cells from touch samples analyzed by flow cytometry.

Stanciu Cristina E CE Kwon Ye Jin YJ Ehrhardt Christopher J CJ

Data in brief 20151219

'Touch' or trace biological samples submitted to caseworking labs as evidence often contain biological material from multiple individuals which can result in mixed DNA profiles. These mixture profiles are difficult to interpret and may cause analytical bottlenecks for forensic laboratories. The data in this brief reports the variation in the relative abundance of intact epithelial cells deposited by four different donors across nine days. Touch samples were generated each day by rubbing a polypr ...[more]

PMID: 26866050

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Project description:The goal of this study was to survey optical and biochemical variation in cell populations deposited onto a surface through touch or contact and identify specific features that may be used to distinguish and then sort cell populations from separate contributors in a trace biological mixture. Although we were not able to detect meaningful biochemical variation in touch samples deposited by different contributors through preliminary antibody surveys, we did observe distinct differences in red autofluorescence emissions (650-670 nm), with as much as a tenfold difference in mean fluorescence intensities observed between certain pairs of donors. Results indicate that the level of red autofluorescence in touch samples can be influenced by a donor's contact with specific material prior to handling the substrate from which cells were collected. In particular, we observed increased red autofluorescence in cells deposited subsequent to handling laboratory gloves, plant material, and certain types of marker ink, which could be easily visualized microscopically or using flow cytometry, and persisted after hand washing. To test whether these observed optical differences could potentially be used as the basis for a cell separation workflow, a controlled two-person touch mixture was separated into two fractions via fluorescence-activated cell sorting (FACS) using gating criteria based on intensity of 650-670 nm emissions and then subjected to DNA analysis. Genetic analysis of the sorted fractions provided partial DNA profiles that were consistent with separation of individual contributors from the mixture suggesting that variation in autofluorescence signatures, even if driven by extrinsic factors, may nonetheless be a useful means of isolating contributors to some touch mixtures. Graphical Abstract Conceptual workflow diagram. Trace biological mixtures containing cells from multiple individuals are analyzed by flow cytometry. Cells are then physically separated into two populations based on intensity of red autofluorescence using Fluorescence Activated Cell Sorting. Each isolated cell fraction is subjected to DNA analysis resulting in a DNA profile for each contributor.

Project description:BackgroundInterest for the study of gut mycobiota in relation with human health and immune homeostasis has increased in the last years. From this perspective, new tools to study the immune/fungal interface are warranted. Systemic humoral immune responses could reflect the dynamic relationships between gut mycobiota and immunity. Using a novel flow cytometry technology (Fungi-Flow) to determine immunoglobulin (Ig) responses to fungi, we studied the relationships between gut mycobiota and systemic humoral anti-commensal immunity.ResultsThe Fungi-Flow method allows a sensitive and specific measurement of systemic IgG responses against 17 commensal and environmental fungi from the two main divisions; Ascomycota and Basidiomycota. IgG responses exhibited a high inter-individual variability. Anti-commensal IgG responses were contrasted with the relative abundance, alpha-diversity, and intra-genus richness of fungal species in gut mycobiota of twenty healthy donors. Categorization of gut mycobiota composition revealed two differentiated fungal ecosystems. Significant difference of anti-Saccharomyces systemic IgG responses were observed in healthy donors stratified according to the fungal ecosystem colonizing their gut. A positive and significant correlation was observed between the variety of IgG responses against fungal commensals and intestinal alpha-diversity. At the level of intra-genus species richness, intense IgG responses were associated with a low intra-genus richness for known pathobionts, but not commensals.ConclusionsFungi-Flow allows an easy and reliable measure of personalized humoral responses against commensal fungi. Combining sequencing technology with our novel Fungi-Flow immunological method, we propose that there are at least two defined ecosystems in the human gut mycobiome associated with systemic humoral responses. Fungi-Flow opens new opportunities to improve our knowledge about the impact of mycobiota in humoral anti-commensal immunity and homeostasis. Video Abstract.

Dataset Information

Forward-scatter and side-scatter dataset for epithelial cells from touch samples analyzed by flow cytometry.

Publications

Forward-scatter and side-scatter dataset for epithelial cells from touch samples analyzed by flow cytometry.

Similar Datasets

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