Evidence map›Paper›PMID 37251119›Full record

ReviewACS omega2023

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques.

Vartika Lohani, Akhiya A R, Soumen Kundu, Md Quasid Akhter, Swarnendu Bag

Abstract readReview
In one paragraph

Review in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
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

5 authors.

Vartika LohaniCSIR Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India.ORCID https://orcid.org/0000-0002-2327-3293
Akhiya A RCSIR Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India.ORCID https://orcid.org/0000-0003-2939-5643
Soumen KunduCSIR Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India.ORCID https://orcid.org/0000-0002-9912-8906
Md Quasid AkhterCSIR Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India.
Swarnendu BagCSIR Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India.ORCID https://orcid.org/0000-0003-2811-482X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Identifiers

PMID37251119
PMCPMC10210017

What OpenQuestion 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 OpenQuestion graph.