Pushing the Boundaries: Forensic DNA Phenotyping Challenged by Single-Cell Sequencing.
Ontology highlight
ABSTRACT:
SUBMITTER: Diepenbroek M
PROVIDER: S-EPMC8466929 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The popularity of dogs as human companions explains why these pets regularly come into focus in forensic cases such as bite attacks or accidents. Canine evidence, e.g., dog hairs, can also act as a link between the victim and suspect in a crime case due to the close contact between dogs and their owners. In line with human DNA identification, dog individualization from crime scene evidence is mainly based on the analysis of short tandem repeat (STR) markers. However, when the DNA profile does not match a reference, additional information regarding the appearance of the dog may provide substantial intelligence value. Key features of the dog's appearance, such as the body size and coat colour are well-recognizable and easy to describe even to non-dog experts, including most investigating officers and eyewitnesses. Therefore, it is reasonable to complement eyewitnesses' testimonies with externally visible traits predicted from associated canine DNA samples. Here, the feasibility and suitability of canine DNA phenotyping is explored from scratch in the form of a proof of concept study. To predict the overall appearance of an unknown dog from its DNA as accurately as possible, the following six traits were chosen: (1) coat colour, (2) coat pattern, (3) coat structure, (4) body size, (5) ear shape, and (6) tail length. A total of 21 genetic markers known for high predicting values for these traits were selected from previously published datasets, comprising 15 SNPs and six INDELS. Three of them belonged to SINE insertions. The experiments were designed in three phases. In the first two stages, the performance of the markers was tested on DNA samples from dogs with well-documented physical characteristics from different breeds. The final blind test, including dogs with initially withheld appearance information, showed that the majority of the selected markers allowed to develop composite sketches, providing a realistic impression of the tested dogs. We regard this study as the first attempt to evaluate the possibilities and limitations of forensic canine DNA phenotyping.
| S-EPMC8230911 | biostudies-literature
Project description:Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.
| S-EPMC5052535 | biostudies-other
Project description:Optical microscopy improves in resolution and signal-to-noise ratio by correcting for the system's point spread function; a measure of how a point source is resolved, typically determined by imaging nanospheres. Optical-resolution optoacoustic (photoacoustic) microscopy could be similarly corrected, especially to account for the spatially-dependent signal distortions induced by the acoustic detection and the time-resolved and bi-polar nature of optoacoustic signals. Correction algorithms must therefore include the spatial dependence of signals' origins and profiles in time, i.e. the four-dimensional total impulse response (TIR). However, such corrections have been so far impeded by a lack of efficient TIR-characterization methods. We introduce high-quality TIR determination based on spatially-distributed optoacoustic point sources (SOAPs), produced by scanning an optical focus on an axially-translatable 250?nm gold layer. Using a spatially-dependent TIR-correction improves the signal-to-noise ratio by >10?dB and the axial resolution by ~30%. This accomplishment displays a new performance paradigm for optoacoustic microscopy.
| S-EPMC7283257 | biostudies-literature
Project description:A custom plate of OpenArray™ technology was evaluated to test 60 single-nucleotide polymorphisms (SNPs) validated for the prediction of eye color, hair color, and skin pigmentation, and for personal identification. The SNPs were selected from already validated subsets (Hirisplex-s, Precision ID Identity SNP Panel, and ForenSeq DNA Signature Prep Kit). The concordance rate and call rate for every SNP were calculated by analyzing 314 sequenced DNA samples. The sensitivity of the assay was assessed by preparing a dilution series of 10.0, 5.0, 1.0, and 0.5 ng. The OpenArray™ platform obtained an average call rate of 96.9% and a concordance rate near 99.8%. Sensitivity testing performed on serial dilutions demonstrated that a sample with 0.5 ng of total input DNA can be correctly typed. The profiles of the 19 SNPs selected for human identification reached a random match probability (RMP) of, on average, 10-8. An analysis of 21 examples of biological evidence from 8 individuals, that generated single short tandem repeat profiles during the routine workflow, demonstrated the applicability of this technology in real cases. Seventeen samples were correctly typed, revealing a call rate higher than 90%. Accordingly, the phenotype prediction revealed the same accuracy described in the corresponding validation data. Despite the reduced discrimination power of this system compared to STR based kits, the OpenArray™ System can be used to exclude suspects and prioritize samples for downstream analyses, providing well-established information about the prediction of eye color, hair color, and skin pigmentation. More studies will be needed for further validation of this technology and to consider the opportunity to implement this custom array with more SNPs to obtain a lower RMP and to include markers for studies of ancestry and lineage.
| S-EPMC7913479 | biostudies-literature
Project description:Forensic science has yet to take full advantage of single cell analysis. Its greatest benefit is the ability to alleviate the challenges associated with DNA mixture analysis, which remains a significant hurdle in forensic science. Many of the factors that cause complexity in mixture interpretation are absent in single cell analyses-multiple contributors, varied levels of contribution, and allele masking. This study revisits single cell analyses in the context of forensic identification, introducing previously unseen depth to the characterization of data generated from single cells using a novel pipeline that includes recovery of single cells using the DEPArray NxT and amplification using the PowerPlex Fusion 6c kit with varied PCR cycles (29, 30, and 31). The resulting allelic signal was assessed using analytical thresholds of 10, 100, and 150RFU. The mean peak heights across the sample sets generally increased as cycle number increased, 75.0 ± 85.3, 147.1 ± 172.6, and 226.1 ± 298.2 RFU, for 29, 30, and 31 cycles, respectively. The average proportion of allele/locus dropout was most significantly impacted by changes in the detection threshold, whereas increases in PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles, less improvement and more volatility was introduced at 31 cycles. The average random match probabilities for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 1018 ± 1.46 × 1019, 1 in 1.49 × 1025 ± 5.8 × 1025, and 1 in 1.83 × 1024 ± 8.09 × 1024, respectively. This demonstrates the current power of single cell analysis in removing the need for complex mixture analysis.
| S-EPMC8007698 | biostudies-literature
Project description:Approximately 81.7 million cats are in 37.5 million U.S. households. Shed fur can be criminal evidence because of transfer to victims, suspects, and/or their belongings. To improve cat hairs as forensic evidence, the mtDNA control region from single hairs, with and without root tags, was sequenced. A dataset of a 402-bp control region segment from 174 random-bred cats representing four U.S. geographic areas was generated to determine the informativeness of the mtDNA region. Thirty-two mtDNA mitotypes were observed ranging in frequencies from 0.6-27%. Four common types occurred in all populations. Low heteroplasmy, 1.7%, was determined. Unique mitotypes were found in 18 individuals, 10.3% of the population studied. The calculated discrimination power implied that 8.3 of 10 randomly selected individuals can be excluded by this region. The genetic characteristics of the region and the generated dataset support the use of this cat mtDNA region in forensic applications.
| S-EPMC3059515 | biostudies-literature
Project description:Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.
| S-EPMC6171490 | biostudies-literature
Project description:MotivationCurrent technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.ResultsWe show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb-comparable to typical gene lengths-compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.Availability and implementationSource code is available at https://www.github.com/raphael-group.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6022575 | biostudies-literature
Project description:Tumors are complex ecosystems harboring multiple cell types which might play a critical role in tumor progression and treatment response. The endometrial epithelial cell identities and immune microenvironment of endometrial carcinoma (ECC) are poorly characterized. In this study, a cellular map of endometrial carcinoma was generated by profiling 30,780 cells isolated from tumor and paratumor tissues from five patients using single-cell RNA sequencing. 7 cell types in lymphocytes, 7 types in myeloid cells and 3 types in endometrial epithelial cells were identified. Distinct CD8+ T cell states and different monocyte-macrophage populations were discovered, among which exhausted CD8+ T cells and macrophages were preferentially enriched in tumor. Both CD8+ T cells and macrophages comport with continuous activation model. Gene expression patterns examination and gene ontology enrichment analysis of endometrial epithelial cells revealed 3 subtypes: stem-like cells, secretory glandular cells and ciliated cells. Overall, our study presents a view of endometrial carcinoma at single-cell resolution that reveals the characteristics of endometrial epithelial cells in the endometrium, and provides a cellular landscape of the tumor immune microenvironment.
| S-EPMC7993685 | biostudies-literature
Project description:MotivationWhile single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance.ResultsWe present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results.Availability and implementationhttps://github.com/elkebir-group/doubletD.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8275324 | biostudies-literature