Unbiased single-cell proteomics (scProteomics) promises to advance our understanding of the cellular composition of complex biological systems. However, a major challenge for current methods is their ability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition method, TIFF (Transferring Identification based on FAIMS Filtering), designed to improve the sensitivity and accuracy of label-free scProteomics. The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently quantified, a significant improvement in overall performance. We applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during a lipopolysaccharide stimulation experiment and uncovered unanticipated temporal response trajectories. Further, we demonstrated, to our knowledge, the first application of scProteomics to classify cell populations of a human organ (the lung) without prior antibody labeling.