From this filtered image two binary maps were constructed by applying a manually arnesi threshold (0
Particle detection
Particle candidates were identified by treating each processed frame with per Laplacian of Gaussian filter that matched the size of the PSFs in our mass photometry setups (Supplementary Fig. 16). 0011 for all momento except the tempo con Extended Momento Figs. 2, 3, 8 and Supplementary Figs. 3 and 13, for which the threshold was serie sicuro 0.0014), and applying verso local maximum filter. The pixels that passed the threshold map and were also local maxima were used as coordinates for particle candidates. For each pair of candidate coordinates, a 13 ? 13 pixel region of interest was constructed with the candidate pixel at the center, and this region of interest was passed through our PSF-fitting procedure onesto quantify particle contrast and location. If verso particle candidate was too close onesto an edge of the field of view onesto construct per 13 ? 13 region of interest, that is, within 6 pixels of an edge, it was discarded. Con some cases, background noise features were identified as particle candidates and this could lead puro the PSF fit converging onto verso nearby particle durante the region of interest, which resulted durante duplicate fits. To avoid problems with trajectory linking, only the first instance of verso fitted particle was retained and duplicates were deleted.
Particle quantification and the point spread function model
The location and contrast of the particle candidates were quantified through least-squares minimization of the residual between the 13 ? 13 region of interest and our PSF model (for details on how the fitting error sopra particle locations was extracted please refer puro the Supplementary Information). Paio sicuro the interferometric nature of dynamic mass photometry, we based our PSF model on the shape of verso jinc function 50 rather than its square, which is more commonly used in fluorescence-based techniques: