pysero enables serological measurements with multiplexed and standard ELISA assays.

The project automates estimation of antibody titers from data collected with ELISA assays performed with antigen-arrays and single antigens.

The immediate goal is to enable specific, sensitive, and quantitative serological surveys for COVID-19.

Brief description

How to analyze the data?


On a typical Winodws, Mac, or Linux computer:

  • Create a conda environment: conda create --name pysero python=3.7
  • Activate conda environment: conda activate pysero
  • Once inside the repository folder, install dependencies: pip install -r requirements.txt

For installation notes for Jetson Nano, see these notes.

The command-line utility "" enables automated analysis.

usage: [-h] (-e | -a) -i INPUT -o OUTPUT
                 [-wf {well_segmentation,well_crop,array_interp,array_fit}]
                 [-d] [-r] [-m METADATA]

optional arguments:
  -h, --help            show this help message and exit
  -e, --extract_od      Segment spots and compute ODs
  -a, --analyze_od      Interpretation, not yet implemented
  -i INPUT, --input INPUT
                        Input directory path
  -o OUTPUT, --output OUTPUT
                        Output directory path, where a timestamped subdir will
                        be generated. In case of rerun, give path to
                        timestamped run directory
  -wf {well_segmentation,well_crop,array_interp,array_fit}, --workflow {well_segmentation,well_crop,array_interp,array_fit}
                        Workflow to automatically identify and extract
                        intensities from experiment. 'Well' experiments are
                        for standard ELISA. 'Array' experiments are for ELISA
                        assays using antigen arrays printed with Scienion
                        Array Printer Default: array_fit
  -d, --debug           Write debug plots of well and spots. Default: False
  -r, --rerun           Rerun wells listed in 'rerun_wells sheets of metadata
                        file. Default: False
  -m METADATA, --metadata METADATA
                        specify the file name for the experiment metadata.
                        Assumed to be in the same directory as images.
                        Default: 'pysero_output_data_metadata.xlsx'

pysero -e -i input -o output will take metadata for antigen array and images as input, and output optical densities for each antigen.
The optical densities are stored in an excel file at the following path: <output>/pysero_<input>_<year><month><day>_<hour><min>/median_ODs.xlsx

If rerunning some of the wells, the input metadata file needs to contain a sheet named 'rerun_wells'
with a column named 'well_names' listing wells that will be rerun.

Collection of jupyter notebooks, such as this, show how to use ODs to evaluate antibody binding.
The interpretation pipeline will soon be accessible as command-line tool.

This workflow describes the steps in the extraction of optical density.

Equipment list

The project aims to implement serological analysis for several antigen multiplexing approaches.

It currently supports:

  • classical ELISA.
  • antigen arrays printed with Scienion.

It can be extended to support:

  • antigen arrays printed with Echo.
  • antigen multiplexing with Luminex beads.

The antigen-arrays can be imaged with:

  • any transmission microscope with motorized XY stage.
  • turn-key plate imagers, e.g., SciReader CL2.
  • Squid - a variant of Octopi platform from Prakash Lab.

The project will also have tools for intersecting data from different assays for estimation of concentrations, determining level of cross-reactivity, ...


Current code is validated for analysis of anigen arrays imaged with Scienion Reader and is being refined for antigen arrays imaged with motorized XY microscope and Squid.