QUANTIFICATION AND STATISTICAL ANALYSIS

Device images were analyzed using a custom script in MATLAB (MathWorks) to automatically detect well location and number of cells per well, extract all signals from each well, and process the data (https://github.com/Miller-JensenLab/Single-Cell-Analysis). In brief, after automatic well and live cell detection, signal image registration, and manual curation, the software automatically extracted the intensity signal from each antibody for all the microwells in the device. This signal across the chip for each individual anti-body was normalized by subtracting a moving Gaussian curve fitted to the local zero-cell well intensity values. A secretion threshold for each antibody was set at the 99th percentile of all normalized zero-cell wells. Data was transformed using the inverse hyperbolic sine with cofactor set at 0.8× secretion threshold.

Data were presented as means ± SEM unless otherwise specified. Statistical analysis was performed by ordinary 2-way ANOVA and the Dunnett method for correction of multiple comparisons as specified in the figure legends. All analyses were performed using Prism 8.4.1 software (GraphPad). For single-cell distributions, statistics were performed using a bootstrapping procedure to calculate the confidence intervals associated with sampling error in single-cell data. To obtain confidence intervals through bootstrapping, the single-cell datasets for each condition were sampled 10,000 times with replacement, and the metric of interest was calculated for each resampled dataset. We then calculated a 95% confidence interval for these resampled datasets, and statistical significance was assigned to pairwise comparisons with non-overlapping confidence intervals. This bootstrapping procedure was done using custom scripts in MATLAB and Python.