Fig 1: Benchmarking CODEX, Related to Figures 1 and 2(A) Experimental scheme for mimicking the tissue with 30 distinct cell types.(B) Montage of a fragment of imaging field of the 15 cycles of CODEX used to render the mix of 30 barcoded spleens – first cycle top left last cycle bottom right.(C) Heatmap (cycles in columns, cells in rows) showing mean fluorescence per cell membrane for each cell per in each of the 15 CODEX cycles performed on cells of 30 barcoded spleens. Odd columns correspond to imaging after labeled base incorporation. Even columns correspond to imaging after inactivation of staining by TCEP.(D) Time-lapse profile of median intensity per cell membrane for individual cells marked by white arrows on (B).(E) Average intensity of CD45 antigen expression in “positive” (blue columns) and “negative” (red columns) cells in 15 CODEX cycles of the experiment. (Similar results were obtained for Cy3-positive populations – data not shown). Linear regression was performed to indicate trends in accumulation of background and signal decline associated with cycle number.(F) Table summarizing CODEX performance stats. Average signal to noise ratio was estimated from ratio of average signal of all positive cells across all cycles to the signal of all “negative” cells across all cycles. Efficiency of fluorophore removal was estimated from average ratio of ([signal after TCEP in cycle N]- [signal after TCEP in cycle (N-1)])/[signal in cycle N] for cells positive in cycle N across all cycles. Average expected signal deterioration was estimated using the trendline equation from (E). Average background accumulation was estimated by fitting linear trendline into the per cycle ratio of average background to average signal (not shown).(G) Image quantification approach used on CODEX data from (A): best focal planes of CODEX stacks were segmented by Cell Profiler. To account for local background the value corresponding to difference between the mean intensity value inside “cell membrane” object (left panel) and the mean intensity inside the external ring object (right panel) was chosen as a representation of the intensity of the antibody signal. In all other experiments custom (see STAR Methods) segmantation developed in this study was used.(H) Sample 500x500 px regions from two samples (BALBc-3 and MRL-4) showing hand-labeled cell centers (yellow crosshairs) and cell outlines detected by the segmentation algorithm (randomly colored).(I) Comparison between the hand-labeled cell identification and algorithm-based algorithm identification, expressed in 3 measures: %Nuclei found (how many of the hand-labeled nuclei centers ended up inside the segmented regions), % Singlets (how many of the cell regions with at least one hand-labeled nuclei center contained exactly one cell center) and %Unlabelled regions (how many segmented regions did not contain a hand-labeled cell center).(J) Summary statistics comparing the segmentation quality between BALBc and MRL/lpr samples.(K) Three step cleanup gating strategy based on 1) stain density (nuclear signal divided by cell size) and profile homogeneity (relative variance of signal from cycle to cycle), 2) removing objects with high background by gating on the signal accrued in “blank”(no stain) cycles 3) constraining the cell size.(L) Percentage of artifactual double-positive cells in CODEX data from sample BALBc-2 (as seen in the upper right quadrant biaxial flow style plots of mutually exclusive lineage markers IgD and CD5) depending on gating and spill compensation.