Data independent acquisition (DIA) is rapidly progressing due to its high degree of accuracy, reproducibility, high-throughput data acquisition and small sample consumption. However, the sensitivity of DIA-MS method on Orbitraps is limited by the relatively slow scan rate and window width. To reach a deeper proteome, multiple dimensional fractionation coupled with data dependent acquisition (DDA) usually consumes 100 µg peptides per sample.
A PulseDIA-MS method was developed to fractionate peptides at MS2 level to maximize the proteome depth with 0.5ug peptides per injection. In PulseDIA, each DIA window was divided into multiple small parts by multiple injections of the same sample with complementary scanning windows. Four parameters were tested and optimized to maximize the performance of PulseDIA: i) number of injections; ii) length of LC gradient; iii) fixed or variable window; iv) the width of overlaps between adjacent sub-windows. HeLa cell digest, breast cancer cell digests were used for the method benchmarking and optimization. The PulseDIA method was then applied to analyze 24 clinical tissue samples from 12 cholangiocellular carcinoma (CCC) patients. All data were analyzed using Spectronaut.
PulseDIA analysis of breast cancer cell sample led to identification of 45,592 peptides and 5,346 proteins using a library containing 60,687 peptide precursors and 6,239 proteins, which is 57.9% and 28.2% higher than conventional DIA method. Five injections of HeLa cell digest increased the peptide and protein identifications by 100.1% and 26.5% respectively compared to conventional DIA. We further applied the PulseDIA method to analyze 24 CCC samples, and quantified 59,887 peptides (58.3% increase) from 5,426 proteins (8.3% increase) using two injections of 0.5 µg peptides. The pulseDIA analysis identified novel regulated proteins in this CCC cohort.
We present a novel DIA method called pulseDIA which allows deeper proteomic analysis using relatively small amount of peptide samples.