MicroMPN

MicroMPN estimates the most probable number (MPN) of viable bacteria from microplate assays to quantify pathogen viability and microbial suppression in mixed communities.


Key Features:

  • High-Throughput Capability: Processes microplate assays to enable rapid screening of microbial communities using replicated dilutions.
  • Fluorescent Labeling Integration (fluorMPN): Incorporates a fluorescently labeled pathogen (fluorMPN) signal to differentiate viable target pathogens from non-target or non-viable microbes.
  • Instrumentation Compatibility: Accepts optical readings from plate readers and is compatible with liquid-handling robotics for automated sample handling and data acquisition.
  • MPN Calculation: Computes MPN values from presence/absence and optical data derived from 96- and 384-well microplate formats.

Scientific Applications:

  • Microbial suppression screening: Quantifies pathogen viability within synthetic bacterial communities to assess suppressive interactions.
  • Soil pathogen studies: Identifies microbial consortia that reduce pathogen levels in complex matrices such as soil.
  • Inoculant and probiotic development: Ranks candidate microbial inoculates or probiotics for control of soil-borne pathogens based on viable pathogen counts.

Methodology:

Analyzes in vitro serial dilutions with replication by recording presence/absence of growth across wells, combines these observations with optical readings from a plate reader to estimate MPN, and requires a Wellmap TOML plate layout file and a CSV file of optical values as inputs.

Topics

Collections

Details

License:
CC0-1.0
Maturity:
Emerging
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux, Mac
Programming Languages:
Python
Added:
9/20/2023
Last Updated:
2/17/2024

Operations

Publications

Franco Meléndez K, Schuster L, Donahey MC, Kairalla E, Jansen MA, Reisch C, Rivers AR. MicroMPN: methods and software for high-throughput screening of microbe suppression in mixed populations. Microbiology Spectrum. 2024;12(3). doi:10.1128/spectrum.03578-23. PMID:38353567. PMCID:PMC10923211.

Funding: - USDA | Agricultural Research Service: 6066-21310-005-00-D

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