One of the most difficult challenges in forensic genetics is interpreting DNA mixes. The current standard workflow and mixture interpretation processes are used to extract DNA from a crime scene sample (e.g., swabs), quantify the extracted DNA (though this step can be skipped in some protocols), amplify targeted Short Tandem Repeat (STR) regions, and detect DNA fragments via Capillary Electrophoresis (CE), Alleles are called with accompanying software, and the contributors’ DNA profiles are interpreted or deconvolved as best as possible from the collection of allelic peaks in an electropherogram, primarily by a DNA analyst(s) based on training and experience, with or without the assistance of probabilistic genotyping software programmes (such as STRmix, LRmix, TrueAllele. Although the DNA may still be present in individual cells when collected in this generalised CE-STR analysis procedure, the cells and therefore the DNA are pooled during extraction. A mixture is formed when more than one contributor to the sample is present. Following the generation of the mixture profile, an analyst(s) attempts to decipher the information in order to determine, for example, the number of contributors (NOC) in a mixture, the genotypes of individual contributors, whether or not a particular individual is or is not a contributor of a mixture, and so on. Given the available information provided by this standard technique, the mixture profile is generally evaluated indirectly, such as inferring the NOC by counting the observed allelic peaks or evaluating the weight of the evidence assuming a person is a contributor vs. The LR compares the likelihoods of seeing the same evidence under two or more competing hypotheses (e.g., the mixture consists of the victim and the suspect vs. the mixture consists of the victim and a random individual in a community). Because to overlapping alleles, allele drop-out (ADO), allele drop-in (ADI), and ambiguity in the NOC, deconvolution of the contributing genotypes might be difficult at times. To date, and most likely in the future, all forensic genetic techniques suffer from these problems. For example, determining the NOC of a three-person combination created by two parents and their kid might be difficult with current standard technique since both alleles of the child are shared with the parents. Furthermore, determining whether a peak in a stutter location of a large contributor allele is made of an allele from a minor contributor plus stutter or purely a stutter result is challenging. As a result, some features of a combination may be difficult to establish with high accuracy. Cells are extracted from forensic materials separately in a single-cell analysis process. Manual micromanipulation, Laser Capture Microdissection (LCM), Magnetic Activated Cell Sorting (MACS) flow cytometry, Fluorescent Activated Cell Sorting (FACS) flow cytometry, and a electrophoresis system (e.g., DEP Array) are all techniques for isolating single cells. Following isolation, single cells can be amplified separately for the targeted areas (e.g., STRs) or treated to Whole Genome Amplification (WGA) before amplification to enrich the DNA targets. The Identifier kit was used to amplify 15 autosomal STRs from 20 single sperm cells from a single donor, and similar work was done on 37 single sperm cells (from three donors) to type 10 autosomal STRs with amplicon sizes of 300 bp. The consensus genotypes in both investigations were compatible with the known donor genotypes. The ADO rate was 25%, while the ADI rate was 1.3 percent. ADO rates were lower (15 percent).

All of these investigations show that single-cell methods can successfully extract STR alleles from single cells. However, no work has offered a systematic way to interpreting STR data from single-cells produced from mixes to far. This is the first research to attempt to create and test interpretation techniques for characterising multiple donors from single-cell profiles of mixtures. The current work examines different interpretation techniques using a simulation approach with various mixing situations and appropriate ADO and ADI rates, and offers results in terms of cell count, NOC, and ADO and ADI rates. The potential of the single-cell technology workflow was evaluated in this study for the probabilities of missing a minor contributor during cell sampling, the accuracy of estimating the NOC with single cells and sampling, the accuracy of recovering the true alleles by single-cell profile consensus, and the capabilities of the single-cell approach for deconvolving mixtures with related contributors. The results demonstrate the great precision that a single cell process for mixture interpretation may attain.