# Machine vision guided robotics for automated microinjections into fruit fly embryos

> **NIH NIH R43** · OBJECTIVE BIOTECHNOLOGY, INC. · 2024 · $294,543

## Abstract

PROJECT SUMMARY
Objective Biotechnology is a spin-out from the University of Minnesota that is pioneering the use of machine
vision guided robots for automating precise microbiology procedures such as microinjection. Drosophila
melanogaster (fruit fly) is a model organism extensively used in both basic and clinical research. A key challenge
in Drosophila biological research is the bottleneck of generating and maintaining transgenic lines of flies.
Traditionally, transgenesis involves skilled technicians performing intricate and precise microinjection
procedures repeatedly. However, Objective Biotechnology Inc. has introduced an innovative solution - a
machine vision guided robot designed to automate the microinjection process for Drosophila
melanogaster embryos. This technology can also be adapted for use with various other organisms. This robot
eliminates the need for manual microinjection protocols, which are operator-dependent, time-consuming, and
require significant training. The robot uses machine learning (ML) models trained to detect individual embryos
on agar plates and guides microinjection needles to perform microinjections at specific locations in each detected
embryo. This robot can be operated by individuals with no prior experience and surpass human capabilities
in terms of microinjection speed, performing at a rate six times faster than humans.
In AIM 1 of this proposal, we will evaluate the efficacy and generalizability of the Autoinjector technology for
transgenesis across a wide spectrum of Drosophila experiments. This involves testing and refining the
automated microinjection process for different genetic backgrounds, microinjection locations, microinjectant
compositions, construct sizes, auxiliary plasmids or transgenes, as well as various operational variables such as
embryo laying conditions, culture media, DNA concentration, and solution viscosity.
AIM 2 of this proposal we will innovate the ML algorithms to address two identified failure modes in the automated
microinjection process. These failure modes are (1) the inability to detect individual embryos when they are
clustered together on the embryo collection plates and (2) the clogging of pipettes during automated
microinjection. We will develop ML-guided robotic algorithms to mitigate these issues and improve the overall
performance of the system.

## Key facts

- **NIH application ID:** 11013464
- **Project number:** 1R43OD037625-01A1
- **Recipient organization:** OBJECTIVE BIOTECHNOLOGY, INC.
- **Principal Investigator:** Andrew D Alegria
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $294,543
- **Award type:** 1
- **Project period:** 2024-09-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11013464

## Citation

> US National Institutes of Health, RePORTER application 11013464, Machine vision guided robotics for automated microinjections into fruit fly embryos (1R43OD037625-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11013464. Licensed CC0.

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