The Addition\nof Transcriptomics to the Bead-Enabled\nAccelerated Monophasic Multi-Omics Method: A Step toward Universal\nSample Preparation

Omics-based measurements enable the study of biomolecules\nin a\nhigh-throughput fashion, leading to the characterization and quantification\nof biological systems. Multi-omics methods aim to incorporate several\nomics measurements for a more holistic approach, which is crucial\nfor advancing our understanding of the diversity and redundancy of\nbiological systems. Current multi-omics sample preparation methods\nhave achieved proteomics, lipidomics, and metabolomics from individual\nsamples; however, the bioinformatic tools currently available for\ninterpreting data generated from these omics are limited. Alternately,\ntranscriptomics has a wide arsenal of available bioinformatic tools\noffering intensive sample characterization but has yet to be incorporated\ninto a unified, multi-omics sample preparation technique. Herein we\ndescribe the modified bead-enabled accelerated monophasic multi-omics\n(mBAMM) method, which incorporates RNA extraction for transcriptomics\nanalysis. mBAMM was shown to enable RNA-seq without compromising the\nisolation of biomolecules for proteomics, lipidomics, and metabolomics.\nThis methodology greatly improves sample characterization and represents\na major innovation toward cohesive insights into biological systems.

Publication Details

Journal
Figshare
Published
2024-10-09
DOI
https://doi.org/10.1021/acs.analchem.4c02835.s001
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Addition\nof Transcriptomics to the Bead-Enabled\nAccelerated Monophasic Multi-Omics Method: A Step toward Universal\nSample Preparation

Figshare
Metabolomics and Mass Spectrometry Studies
article

The Addition\nof Transcriptomics to the Bead-Enabled\nAccelerated Monophasic Multi-Omics Method: A Step toward Universal\nSample Preparation

article en

Abstract

Omics-based measurements enable the study of biomolecules\nin a\nhigh-throughput fashion, leading to the characterization and quantification\nof biological systems. Multi-omics methods aim to incorporate several\nomics measurements for a more holistic approach, which is crucial\nfor advancing our understanding of the diversity and redundancy of\nbiological systems. Current multi-omics sample preparation methods\nhave achieved proteomics, lipidomics, and metabolomics from individual\nsamples; however, the bioinformatic tools currently available for\ninterpreting data generated from these omics are limited. Alternately,\ntranscriptomics has a wide arsenal of available bioinformatic tools\noffering intensive sample characterization but has yet to be incorporated\ninto a unified, multi-omics sample preparation technique. Herein we\ndescribe the modified bead-enabled accelerated monophasic multi-omics\n(mBAMM) method, which incorporates RNA extraction for transcriptomics\nanalysis. mBAMM was shown to enable RNA-seq without compromising the\nisolation of biomolecules for proteomics, lipidomics, and metabolomics.\nThis methodology greatly improves sample characterization and represents\na major innovation toward cohesive insights into biological systems.

Figshare
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Metabolomics and Mass Spectrometry Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Addition\nof Transcriptomics to the Bead-Enabled\nAccelerated Monophasic Multi-Omics Method: A Step toward Universal\nSample Preparation · Figshare (2024) | TGRS Research Map | TGRS