Viral infection outcomes vary significantly across cell types and is influenced by various factors such as infection levels and single-cell heterogeneity. Recently, technological developments in mass spectrometry-based single cell proteomics has made identification of +1,500 proteins routine, and has made functional biological analysis of single cells possible on the protein and PTM-level. However, single cell LC-MS/MS data remains constrained by fundamental challenges in identification, quantification, and interpretation requiring the development of tailored computational strategies. To address these challenges, I will focus on improving the identification of low-abundant and modified peptides, and integrate these in DDA and DIA identification workflows. Additionally, I will incorporate identification uncertainty into quantification models to improve the reliability of downstream analyses while accounting for both technical and biological variation. These identification and quantification advancements will be applied to a single-cell, multi-omics dataset of coronavirus-infected cells, providing deeper insights into how coronavirus infection reshapes host proteomes.