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High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy

Award Number: R01HL163513
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2023
PERIOD OF PERFORMANCE END DATE: 02/28/2027

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High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy - PROJECT SUMMARY/ABSTRACT Challenges. The airway epithelium consists of various cell types – understanding cellular and functional heterogeneity will have a significant impact on diagnosing and treating diseases. However, few analytical tools are available to investigate spatiotemporal phenotypes of these cells on a global population scale. Conventional high-throughput microscopy (HTM), although powerful for dissecting heterogeneous biological processes, is significantly limited in multiscale imaging and analytics. Most HTM systems are constructed by combining high-magnification microscopes with scanning stages; this configuration would entail high complexity in the system design and operation, high cost, and slow image acquisition rates. Follow-on data analyses, based on traditional ensemble averaging approaches, often lead to the loss of detailed mechanistic information. Innovations. We will advance a “smart” imaging platform, M3 (Multiscale Machine-learning Microscopy) for large-scale, live-cell analyses. M3 will integrate cutting-edge breakthroughs: Fourier ptychographic microscopy (FPM) and deep learning (DL). FPM is based on a spatially coded-illumination technique, collecting low-resolution image sequences while changing the position of a point-light source. These images are then numerically combined to restore the whole Fourier space, allowing FPM to achieve both wide field-of-view and high spatial resolution simultaneously. DL is potent in discovering intricate, hidden structures in high-dimensional data sets with limited human supervision. We will integrate DL with time-series modeling to learn disease-related cellular traits. Goals. We will implement the M3 platform and adopt it to analyze cellular phenotypes during airway epithelium development. Aim 1. We will construct the M3 imaging system based on the FPM technology. This system will feature i) a new numerical algorithm to reconstruct 3D volumetric images and ii) multi-color imaging capacity for molecular detection. Aim 2. We will advance a DL framework for M3 image analyses. This framework will be designed to recognize different cell types and learn their spatiotemporal features to unravel multiscale cellular heterogeneity. Aim 3. We will apply M3 to phenotype cells in the airway epithelium. We will use an in-vitro model that uses induced pluripotent stem cells (iPSCs) to derive lung epithelium. M3 will monitor cellular differentiation during epithelium development and examine the correlation between cellular phenotypes and functionals. Impact. The M3 will bring unprecedented analytical power to characterize diverse cells within the airway epithelium, allowing us to discover many hidden phenotypes in cellular and tissue levels. Such knowledge would have implications for early disease detection as well as designing effective therapeutics.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $764,694 )
2026	2026	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	000	4	3/3/2026	NON-COMPETING CONTINUATION	$741,285
2026	2026	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	001	4	3/25/2026	NON-COMPETING CONTINUATION	$23,409
														Subtotal = $764,694

Issue Date FY: 2025 ( Subtotal = $780,300 )
2025	2025	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	000	3	4/23/2025	NON-COMPETING CONTINUATION	$702,271
2025	2025	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	001	3	9/18/2025	NON-COMPETING CONTINUATION	$78,029
														Subtotal = $780,300

Issue Date FY: 2024 ( Subtotal = $759,296 )
2024	2024	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	000	2	5/24/2024	NON-COMPETING CONTINUATION	$759,296
														Subtotal = $759,296

Issue Date FY: 2023 ( Subtotal = $781,190 )
2023	2023	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	000	1	3/23/2023	NEW	$781,190
														Subtotal = $781,190

Grand Total All Awards = $3,085,480

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High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy

Award Number: R01HL163513

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/01/2023

PERIOD OF PERFORMANCE END DATE: 02/28/2027

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