Evidence map›Paper›PMID 40501910›Full record

ArticlebioRxiv : the preprint server for biology2025

Increasing mass spectrometry throughput using time-encoded sample multiplexing.

Jason Derks, Kevin McDonnell, Nathan Wamsley, Peyton Stewart, Maddy Yeh, Harrison Specht, Nikolai Slavov

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jason DerksParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0000-0001-9727-9105
Kevin McDonnellParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0000-0002-3428-8239
Nathan WamsleyParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0000-0002-1119-4395
Peyton StewartParallel Squared Technology Institute, Watertown, MA 02472, USA.
Maddy YehParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0009-0002-5597-8914
Harrison SpechtParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0000-0003-3151-6803
Nikolai SlavovParallel Squared Technology Institute, Watertown, MA 02472, USA.ORCID 0000-0003-2035-1820

Funding

Molecular and metabolic influences on the activation of monocytes and macrophages at single-cell resolutionR35GM148218 · NIGMS · NORTHEASTERN UNIVERSITY · PI Nikolai Slavov · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM148218
6 · The paper itself

Abstract

Liquid chromatography-mass spectrometry (LC-MS) can enable precise and accurate quantification of analytes at high-sensitivity, but the rate at which samples can be analyzed remains limiting. Throughput can be increased by multiplexing samples in the mass domain with plexDIA, yet multiplexing along one dimension will only linearly scale throughput with plex. To enable combinatorial-scaling of proteomics throughput, we developed a complementary multiplexing strategy in the time domain, termed 'timePlex'. timePlex staggers and overlaps the separation periods of individual samples. This strategy is orthogonal to isotopic multiplexing, which enables combinatorial multiplexing in mass and time domains when paired together, and thus multiplicatively increased throughput. We demonstrate this with 3-timePlex and 3-plexDIA, enabling the multiplexing of 9 samples per LC-MS run, and 3-timePlex and 9-plexDIA exceeding 500 samples / day with a combinatorial 27-plex. Crucially, timePlex supports sensitive analyses, including of single cells. These results establish timePlex as a methodology for label-free multiplexing and combinatorial scaling of the throughput of LC-MS proteomics. We project this combined approach will eventually enable an increase in throughput exceeding 1,000 samples / day.

Identifiers

PMID40501910
PMCPMC12154682

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.