Bacterial infections impose a costly health burden worldwide which is compounded by the alarming increase of multi-drug resistant bacteria. Hence, there is an urgent unmet need for developing new antibiotics with higher efficacy. Bioluminescence imaging (BLI) is a tool for non-invasively studying bacterial infections in small animal models aiding the development of new therapies. BLI faces, however, several limitations regarding data quantification and reproducibility, liabilities in translational research. It cannot systematically quantify in vivo bacterial organ burden across the cohort, and longitudinally. Furthermore, BLI analysis is still highly operator- dependent resulting in poor data analysis reproducibility and high variability. Therefore, InVivo Analytics proposes to develop InVivoPLOT. It is a hardware add-on for BLI systems that is seamlessly integrated to a cloud-based 3D reconstruction and automated data analysis software. InVivoPLOT permits for autonomous quantification of spatial bacterial burden across the cohort longitudinally. In Phase 1, InVivo Analytics developed a novel Body-Conforming Animal Mold (BCAM), an optical calibrator, and an Organ Probability Map (OPM); and measured quantitatively the in vivo bacterial density distribution of a urinary tract infection (UTI) model. Phase II will expand on those accomplishments by developing the automated BLI analysis software, which will eliminate operator-dependent variability and error, increase reproducibility, and permit quantification of BLI studies. A client applet will up-load BLI data to the cloud and 3D images of the bioluminescent bacteria distribution will be computed along with quality metrics, automatically co-registered to an OPM, and the 3D image data sets will be digitally analyzed while enabling Digital Organ Harvesting. The standardizing BCAM allows for automated cluster analysis to objectively separate blinded datasets into quantitatively correlated cohorts, both spatially and temporally. A comprehensive, unbiased study report is sent back to the investigator. Phase 2 will demonstrate the feasibility of InVivoPLOT's analysis software and hardware for providing quantitative imaging data with high reproducibility across different animals and time points.
In Aim 1, the BCAM and OPM will be expanded to different mouse size, sex, and strains.
In Aim 2, a set of different mathematical analysis tools on a cloud-based architecture will provide a rapid and operator-independent data analysis.
In Aim 3, the analysis tools, BCAMs, and OPMs will be tested on a UTI model. The successful completion of the proposed project will help to commercialize InVivoPLOT and will find immediate application in the pharmaceutical industry for rapid development of novel antibiotics. It will provide a tool for in vivo quantification of bioluminescent targets, operator independent data analysis, and online knowledge base of directly comparable data using a consistent format while increasing reproducibility to improve and accelerate disease interventions. The long-term goal is to extend the unit's utility to a number of other fields needing to quantify bioluminescent targets such as in neurobiology, oncology, and stem cell research.

Public Health Relevance

In this Phase II SBIR, InVivo Analytics seeks funding for the development of InVivoPLOT to monitor animal models of infection in real time. Despite its prevalence, bacterial infections, particularly of the urinary tract, are a significant burden on th health care system because of the lack of new antibiotics being developed. InVivo Analytics will develop a new imaging tool and analysis software, which can automatically quantify bacterial infection in the living animal, thereby allowing accurate development of novel antibiotics.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-SBIB-T (10))
Program Officer
Shabestari, Behrouz
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
In Vivo Analytics, Inc.
New York
United States
Zip Code
Klose, Alexander D; Paragas, Neal (2018) Automated quantification of bioluminescence images. Nat Commun 9:4262