| Title |
A robust mass spectrometry method for rapid profiling of erythrocyte ghost membrane proteomes
|
|---|---|
| Published in |
Clinical Proteomics, March 2018
|
| DOI | 10.1186/s12014-018-9190-4 |
| Pubmed ID | |
| Authors |
Haddy K. S. Fye, Paul Mrosso, Lesley Bruce, Marie-Laëtitia Thézénas, Simon Davis, Roman Fischer, Gration L. Rwegasira, Julie Makani, Benedikt M. Kessler |
| Abstract |
Red blood cell (RBC) physiology is directly linked to many human disorders associated with low tissue oxygen levels or anemia including chronic obstructive pulmonary disease, congenital heart disease, sleep apnea and sickle cell anemia. Parasites such asPlasmodiumspp. andphylum Apicomplexadirectly target RBCs, and surface molecules within the RBC membrane are critical for pathogen interactions. Proteomics of RBC membrane 'ghost' fractions has therefore been of considerable interest, but protocols described to date are either suboptimal or too extensive to be applicable to a larger set of clinical cohorts. Here, we describe an optimised erythrocyte isolation protocol from blood, tested for various storage conditions and explored using different fractionation conditions for isolating ghost RBC membranes. Liquid chromatography mass spectrometry (LC-MS) analysis on a Q-Exactive Orbitrap instrument was used to profile proteins isolated from the comparative conditions. Data analysis was run on the MASCOT and MaxQuant platforms to assess their scope and diversity. The results obtained demonstrate a robust method for membrane enrichment enabling consistent MS based characterisation of > 900 RBC membrane proteins in single LC-MS/MS analyses. Non-detergent based membrane solubilisation methods using the tissue and supernatant fractions of isolated ghost membranes are shown to offer effective haemoglobin removal as well as diverse recovery including erythrocyte membrane proteins of high and low abundance. The methods described in this manuscript propose a medium to high throughput framework for membrane proteome profiling by LC-MS of potential applicability to larger clinical cohorts in a variety of disease contexts. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 55 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 18% |
| Student > Bachelor | 7 | 13% |
| Researcher | 6 | 11% |
| Student > Master | 6 | 11% |
| Other | 5 | 9% |
| Other | 11 | 20% |
| Unknown | 10 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 22% |
| Medicine and Dentistry | 10 | 18% |
| Agricultural and Biological Sciences | 9 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Engineering | 2 | 4% |
| Other | 6 | 11% |
| Unknown | 13 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 April 2018.
All research outputs
#17,934,709
of 23,028,364 outputs
Outputs from Clinical Proteomics
#203
of 285 outputs
Outputs of similar age
#241,522
of 332,402 outputs
Outputs of similar age from Clinical Proteomics
#7
of 7 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 285 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,402 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.