Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues

蛋白质组学 图谱 计算生物学 生物 基因表达谱 编码 基因 定量蛋白质组学 转录组 基因表达 蛋白质表达 遗传学
作者
Evelina Sjöstedt,Åsa Sivertsson,Feria Hikmet,Borbala Katona,Åsa Näsström,Jimmy Vuu,Dennis Kesti,Per Oksvold,Per-Henrik Edqvist,Ing-Marie Olsson,Mathias Uhlen,Cecilia Lindskog
出处
期刊:Journal of Proteome Research [American Chemical Society]
卷期号:17 (12): 4127-4137 被引量:14
标识
DOI:10.1021/acs.jproteome.8b00406
摘要

A large portion of human proteins are referred to as missing proteins, defined as protein-coding genes that lack experimental data on the protein level due to factors such as temporal expression, expression in tissues that are difficult to sample, or they actually do not encode functional proteins. In the present investigation, an integrated omics approach was used for identification and exploration of missing proteins. Transcriptomics data from three different sources—the Human Protein Atlas (HPA), the GTEx consortium, and the FANTOM5 consortium—were used as a starting point to identify genes selectively expressed in specialized tissues. Complementing the analysis with profiling on more specific tissues based on immunohistochemistry allowed for further exploration of cell-type-specific expression patterns. More detailed tissue profiling was performed for >300 genes on complementing tissues. The analysis identified tissue-specific expression of nine proteins previously listed as missing proteins (POU4F1, FRMD1, ARHGEF33, GABRG1, KRTAP2-1, BHLHE22, SPRR4, AVPR1B, and DCLK3), as well as numerous proteins with evidence of existence on the protein level that previously lacked information on spatial resolution and cell-type-specific expression pattern. We here present a comprehensive strategy for identification of missing proteins by combining transcriptomics with antibody-based proteomics. The analyzed proteins provide interesting targets for organ-specific research in health and disease.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
orixero应助NIES采纳,获得10
刚刚
彭于晏应助机智的小蘑菇采纳,获得10
2秒前
SciGPT应助bofu采纳,获得10
3秒前
Lucas应助嘎嘎能睡采纳,获得10
3秒前
4秒前
小云飘飘完成签到,获得积分10
6秒前
8秒前
ss25发布了新的文献求助10
9秒前
9秒前
11给11的求助进行了留言
9秒前
亵渎发布了新的文献求助10
10秒前
俊秀的元灵应助Singularity采纳,获得10
10秒前
11秒前
Gaopkid完成签到,获得积分10
12秒前
13秒前
ww发布了新的文献求助10
14秒前
李半斤完成签到,获得积分10
14秒前
Gaopkid发布了新的文献求助10
15秒前
断鸿发布了新的文献求助10
16秒前
bofu发布了新的文献求助10
16秒前
亵渎完成签到,获得积分10
16秒前
Tine关注了科研通微信公众号
17秒前
18秒前
田様应助研友_n2KyPZ采纳,获得10
18秒前
木兮关注了科研通微信公众号
19秒前
19秒前
20秒前
刘晨阳发布了新的文献求助10
21秒前
嘎嘎能睡发布了新的文献求助10
22秒前
别来无恙发布了新的文献求助100
22秒前
bofu发布了新的文献求助10
23秒前
mm完成签到,获得积分10
24秒前
27秒前
研友_n2KyPZ完成签到,获得积分10
28秒前
28秒前
30秒前
30秒前
科目三应助bofu采纳,获得10
30秒前
Mike001发布了新的文献求助10
32秒前
33秒前
高分求助中
Manual of Clinical Microbiology, 4 Volume Set (ASM Books) 13th Edition 1000
Sport in der Antike 800
Aspect and Predication: The Semantics of Argument Structure 666
De arte gymnastica. The art of gymnastics 600
少脉山油柑叶的化学成分研究 530
Electronic Structure Calculations and Structure-Property Relationships on Aromatic Nitro Compounds 500
Berns Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2411475
求助须知:如何正确求助?哪些是违规求助? 2106328
关于积分的说明 5322886
捐赠科研通 1833874
什么是DOI,文献DOI怎么找? 913812
版权声明 560875
科研通“疑难数据库(出版商)”最低求助积分说明 488598