Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Han-xiao Wang , Xiao-zhao Liu , Xi-miao He , Chao Xiao , Dai-xin Huang , Shao-hua Yi

Current Medical Science ›› 2023, Vol. 43 ›› Issue (5) : 908 -918.

PDF
Current Medical Science ›› 2023, Vol. 43 ›› Issue (5) : 908 -918. DOI: 10.1007/s11596-023-2770-1
Original Article

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Author information +
History +
PDF

Abstract

Objective

Body fluid mixtures are complex biological samples that frequently occur in crime scenes, and can provide important clues for criminal case analysis. DNA methylation assay has been applied in the identification of human body fluids, and has exhibited excellent performance in predicting single-source body fluids. The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification, and accurately predict the mixture samples. In addition, the value of DNA methylation in the prediction of body fluid mixtures was further explored.

Methods

In the present study, 420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system. Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.

Results

Significant differences in methylation levels were observed between the mixtures and single body fluids. For all kinds of mixtures, the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions (1:20, 1:10, 1:5, 1:1, 5:1, 10:1 and 20:1). Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components, based on the methylation levels of 10 markers. For the mixture prediction, Model-1 presented outstanding prediction accuracy, which reached up to 99.3% in 427 training samples, and had a remarkable accuracy of 100% in 243 independent test samples. For the mixture proportion prediction, Model-2 demonstrated an excellent accuracy of 98.8% in 252 training samples, and 98.2% in 168 independent test samples. The total prediction accuracy reached 99.3% for body fluid mixtures and 98.6% for the mixture proportions.

Conclusion

These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Keywords

body fluid identification / mixture / mixing ratio / DNA methylation / multiplex assay / random forest model

Cite this article

Download citation ▾
Han-xiao Wang, Xiao-zhao Liu, Xi-miao He, Chao Xiao, Dai-xin Huang, Shao-hua Yi. Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models. Current Medical Science, 2023, 43(5): 908-918 DOI:10.1007/s11596-023-2770-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

VirklerK, LednevIK. Analysis of body fluids for forensic purposes: From laboratory testing to nondestructive rapid confirmatory identification at a crime scene. Forensic Sci Int, 2009, 188(1–3): 1-17

[2]

WatanabeK, TaniguchiK, AkutsuT. Development of a DNA methylation-based semen-specific SNP typing method: A new approach for genotyping from a mixture of body fluids. Forensic Sci Int Genet, 2018, 37: 227-234

[3]

IngoldS, DørumG, HansonE, et al.. Body fluid identification and assignment to donors using a targeted mRNA massively parallel sequencing approach–results of a second EUROFORGEN/EDNAP collaborative exercise. Forensic Sci Int Genet, 2020, 45: 102208

[4]

IngoldS, DørumG, HansonE, et al.. Assigning forensic body fluids to donors in mixed body fluids by targeted RNA/DNA deep sequencing of coding region SNPs. Int J Legal Med, 2020, 134(2): 473-485

[5]

LiuJ, ChengX, LiuF, et al.. Identification of coding region SNPs from specific and sensitive mRNA biomarkers for the deconvolution of the semen donor in a body fluid mixture. Forensic Sci Int Genet, 2021, 52: 102483

[6]

UchimotoML, BeasleyE, CoultN, et al.. Considering the effect of stem-loop reverse transcription and real-time PCR analysis of blood and saliva specific microRNA markers upon mixed body fluid stains. Forensic Sci Int Genet, 2013, 7(4): 418-421

[7]

HoltkötterH, SchwenderK, WiegandP, et al.. Improving body fluid identification in forensic trace evidence—construction of an immunochromatographic test array to rapidly detect up to five body fluids simultaneously. Int J Legal Med, 2018, 132(1): 83-90

[8]

YaoT, HanX, GuanT, et al.. Exploration of the microbiome community for saliva, skin, and a mixture of both from a population living in Guangdong. Int J Legal Med, 2021, 135(1): 53-62

[9]

FujimotoS, HamanoY, IchiokaK, et al.. Rapid semen identification from mixed body fluids using methylation-sensitive high-resolution melting analysis of the DACT1 gene. Leg Med, 2021, 48: 101806

[10]

SongF, LuoH, HouY. Developed and evaluated a multiplex mRNA profiling system for body fluid identification in Chinese Han population. J Forensic Leg Med, 2015, 35: 73-80

[11]

ZhangY, LiuB, ShaoC, et al.. Evaluation of the inclusion of circular RNAs in mRNA profiling in forensic body fluid identification. Int J Legal Med, 2018, 132(1): 43-52

[12]

OLK, GlynnCL. Investigating the Isolation and Amplification of microRNAs for Forensic Body Fluid Identification. Microrna, 2018, 7(3): 187-194

[13]

WangS, WangZ, TaoR, et al.. The potential use of Piwi-interacting RNA biomarkers in forensic body fluid identification: A proof-of-principle study. Forensic Sci Int Genet, 2019, 39: 129-135

[14]

LiuB, YangQ, MengH, et al.. Development of a multiplex system for the identification of forensically relevant body fluids. Forensic Sci Int Genet, 2020, 47: 102312

[15]

WangSY, TaoRY, HouYP, et al.. Application and Prospect of RNA Profiling Analysis in Forensic Body Fluid Identification. Fa Yi Xue Za Zhi (Chinese), 2022, 38(6): 763-773

[16]

FrumkinD, WasserstromA, BudowleB, et al.. DNA methylation-based forensic tissue identification. Forensic Sci Int Genet, 2011, 5(5): 517-524

[17]

ParkJ, KwonO, KimJH, et al.. Identification of body fluid-specific DNA methylation markers for use in forensic science. Forensic Sci Int Genet, 2014, 13: 147-153

[18]

LeeHY, AnJH, JungS, et al.. Genome-wide methylation profiling and a multiplex construction for the identification of body fluids using epigenetic markers. Forensic Sci Int Genet, 2015, 17: 17-24

[19]

SilvaDSBS, AntunesJ, BalamuruganK, et al.. Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples. Forensic Sci Int Genet, 2016, 23: 55-63

[20]

HoltkötterH, BeyerV, SchwenderK, et al.. Independent validation of body fluid-specific CpG markers and construction of a robust multiplex assay. Forensic Sci Int Genet, 2017, 29: 261-268

[21]

KaderF, GhaiM, OlaniranAO. Characterization of DNA methylation-based markers for human body fluid identification in forensics: a critical review. Int J Legal Med, 2020, 134(1): 1-20

[22]

XieB, SongF, WangS, et al.. Exploring a multiplex DNA methylation-based SNP typing method for body fluids identification: As a preliminary report. Forensic Sci Int, 2020, 313: 110329

[23]

VidakiA, GiangasparoF, Syndercombe CourtD. Discovery of potential DNA methylation markers for forensic tissue identification using bisulphite pyrosequencing. Electrophoresis, 2016, 37(21): 2767-2779

[24]

WatanabeK, TaniguchiK, ToyomaneK, et al.. A new approach for forensic analysis of saliva-containing body fluid mixtures based on SNPs and methylation patterns of nearby CpGs. Forensic Sci Int Genet, 2022, 56: 102624

[25]

LeeHY, ParkMJ, ChoiA, et al.. Potential forensic application of DNA methylation profiling to body fluid identification. Int J Legal Med, 2012, 126(1): 55-62

[26]

ForatS, HuettelB, ReinhardtR, et al.. Methylation Markers for the Identification of Body Fluids and Tissues from Forensic Trace Evidence. PLoS One, 2016, 11(2): e0147973

[27]

DanaherP, WhiteRL, HansonEK, et al.. Facile semi-automated forensic body fluid identification by multiplex solution hybridization of NanoString® barcode probes to specific mRNA targets. Forensic Sci Int Genet, 2015, 14: 18-30

[28]

de ZoeteJ, CurranJ, SjerpsM. A probabilistic approach for the interpretation of RNA profiles as cell type evidence. Forensic Sci Int Genet, 2016, 20: 30-44

[29]

SauerE, ReinkeA, CourtsC. Differentiation of five body fluids from forensic samples by expression analysis of four microRNAs using quantitative PCR. Forensic Sci Int Genet, 2016, 22: 89-99

[30]

TianH, BaiP, TanY, et al.. A new method to detect methylation profiles for forensic body fluid identification combining ARMS-PCR technique and random forest model. Forensic Sci Int Genet, 2020, 49: 102371

[31]

HoTK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell, 1998, 20(8): 832-844

[32]

HuangH, LiuX, ChengJ, et al.. A novel multiplex assay system based on 10 methylation markers for forensic identification of body fluids. J Forensic Sci, 2022, 67(1): 136-148

[33]

VidakiA, KayserM. From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence. Genome Biol, 2017, 18(1): 238

[34]

DørumG, IngoldS, HansonE, et al.. Predicting the origin of stains from next generation sequencing mRNA data. Forensic Sci Int Genet, 2018, 34: 37-48

AI Summary AI Mindmap
PDF

97

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/