Determination of the Time since Deposition of Blood Traces Utilizing a Liquid Chromatography–Mass Spectrometry-Based Proteomics Approach

化学 色谱法 主成分分析 质谱法 多元统计 刀切重采样 蛋白质组学 样品(材料) 蛋白质组 线性判别分析 层次聚类 液相色谱-质谱法 多元分析 分析化学(期刊) 聚类分析 人工智能 统计 计算机科学 数学 基因 生物化学 估计员
作者
Tom D. Schneider,Bernd Roschitzki,Jonas Grossmann,Thomas Kræmer,Andrea E. Steuer
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:94 (30): 10695-10704 被引量:16
标识
DOI:10.1021/acs.analchem.2c01009
摘要

Knowledge about when a bloodstain was deposited at a crime scene can be of critical value in forensic investigation. A donor of a genetically identified bloodstain could be linked to a suspected time frame and the crime scene itself. Determination of the time since deposition (TsD) has been extensively studied before but has yet to reach maturity. We therefore conducted a proof-of-principle study to study time- and storage-dependent changes of the proteomes of dried blood stains. A bottom-up proteomics approach was employed, and high-resolution liquid-chromatography–mass-spectrometry (HR-LC–MS) and data-independent acquisition (DIA) were used to analyze samples aged over a 2 month period and two different storage conditions. In multivariate analysis, samples showed distinct clustering according to their TsD in both principal component analysis (PCA) and in partial least square discriminant analysis (PLS DA). The storage condition alters sample aging and yields different separation-driving peptides in hierarchical clustering and in TsD marker peptide selection. Certain peptides and amino acid modifications were identified and further assessed for their applicability in assessing passed TsD. A prediction model based on data resampling (Jackknife) was applied, and prediction values for selected peptide ratios were created. Depending on storage conditions and actual sample age, mean prediction performances ranges in between 70 and 130% for the majority of peptides and time points. This places this study as a first in investigating LC–MS based bottom-up proteomics approaches for TsD determination.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
内向井发布了新的文献求助10
刚刚
星辰完成签到,获得积分10
刚刚
刚刚
1秒前
ccc发布了新的文献求助10
1秒前
希望天下0贩的0应助czz采纳,获得10
2秒前
2秒前
lnan发布了新的文献求助10
2秒前
2秒前
东郭雁梅发布了新的文献求助10
3秒前
深情安青应助Aurora采纳,获得10
3秒前
别斑秃了完成签到 ,获得积分10
3秒前
3秒前
wheeler1完成签到,获得积分10
3秒前
打打应助科研通管家采纳,获得10
3秒前
Return应助科研通管家采纳,获得10
3秒前
英俊的铭应助科研通管家采纳,获得10
4秒前
Ava应助科研通管家采纳,获得10
4秒前
寻道图强应助科研通管家采纳,获得30
4秒前
annie应助科研通管家采纳,获得10
4秒前
科研通AI2S应助科研通管家采纳,获得10
4秒前
天天快乐应助科研通管家采纳,获得10
4秒前
李健应助科研通管家采纳,获得10
4秒前
汉堡包应助科研通管家采纳,获得10
4秒前
5秒前
研友_VZG7GZ应助科研通管家采纳,获得10
5秒前
Andd完成签到,获得积分10
5秒前
左秋白完成签到,获得积分10
5秒前
Wind应助科研通管家采纳,获得10
5秒前
寻道图强应助科研通管家采纳,获得40
5秒前
科研通AI2S应助科研通管家采纳,获得10
5秒前
5秒前
慕青应助科研通管家采纳,获得10
5秒前
废人一个发布了新的文献求助10
5秒前
浮游应助科研通管家采纳,获得10
5秒前
CodeCraft应助科研通管家采纳,获得10
6秒前
852应助科研通管家采纳,获得10
6秒前
6秒前
思源应助科研通管家采纳,获得10
6秒前
6秒前
高分求助中
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 12000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5695511
求助须知:如何正确求助?哪些是违规求助? 5102149
关于积分的说明 15216311
捐赠科研通 4851790
什么是DOI,文献DOI怎么找? 2602705
邀请新用户注册赠送积分活动 1554389
关于科研通互助平台的介绍 1512420