Supplementary Figure 6: Image-processing pipeline.
From: Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
Overview of computational analysis to (1) identify fluorescent peptides as peaks in TIRF images, (2) align imaged peaks across consecutive Edman cycles, (3) identify the cycle at which each dye is removed, retaining “well-behaved” peptides. Dye positions are assigned using a maximum likelihood statistical model (see Online Methods), based on the empirical observation (4) that fluorescence intensities for one dye are log-normally distributed with a (log-normal) mean μ and standard deviation σ. Intensities for higher numbers of dyes are well-fit by log-normals with mean μ + ln(dye count) – dye-dye interaction factor Qc QUOTEμ+ln(dye count) -dye-dye interaction factorQ c , and standard deviation σ (Supplementary Fig. 9). (5) For each peptide, the number of dyes present after each Edman cycle is inferred by fitting observed intensities to each of the possible monotonically decreasing step functions (for up to 5 dyes), selecting the function maximizing a quality of fit scored using th lognormal probability density functions. (6) Counts of individual molecules exhibiting different step drop patterns are summarized in histograms.
