DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

Abstract

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

Morphological single-cell analysis of peripheral blood mononuclear cells from 390 healthy blood donors with Blood Cell Painting

“…We envision our BCP data to facilitate self-supervised training of very large encoder models (Bommasani et al 2021, Pfaendler et al 2023) for modeling morphological and functional characteristics of PBMCs, leading to the discovery of novel genotype-phenotype associations and blood cell trait determinants. Another exciting direction is training label-free models for inexpensive morphological profiling (Cross-Zamirski et al 2022, Timonen et al 2023) with large BCP datasets. Our results will pave the way for understanding the causes and consequences of hematological phenotypes in disease and health by contributing a large-scale morphological characterization of immune cells in healthy individuals.…”

Advancing precision single-cell analysis of red blood cells through semi-supervised deep learning using database of patients with post-COVID-19 syndrome

“…DL has revolutionized the field of image analysis, offering unprecedented capabilities in identifying and classifying complex patterns within visual data. Its importance in the cell image analysis is particularly noteworthy 13,35 . Traditional image analysis techniques, while useful, often struggle with the high dimensionality and variability inherent in cellular images.…”

“…Prior contributions in this field successfully attempted the integration of machine learning algorithms, particularly deep learning, resulting in significant results in classification accuracy and data processing efficiency. However, most of the past works rely on heavy computation models for both classification and localization 26,32,35 Additionally, there has been no mention of poorly focused datasets and attempts to solve the issue of defocus for IFC data. Additionally, past works typically do not optimize different pre-training modes and model configurations 8,22,35,40 .…”

“…However, most of the past works rely on heavy computation models for both classification and localization 26,32,35 Additionally, there has been no mention of poorly focused datasets and attempts to solve the issue of defocus for IFC data. Additionally, past works typically do not optimize different pre-training modes and model configurations 8,22,35,40 .…”

PXPermute: Unveiling staining importance in multichannel fluorescence microscopy

“…Machine learning methods, such as image classification, promise to deliver accurate, consistent, fast, and reliable predictions [11][12][13] . A few open-source libraries have recently been published, specializing in machine learning for IFC analysis 10,11,14,15 . These libraries provide in-model interpretability like random forest 16 or post-model interpretability using methods such as Grad-CAM 17 for convolutional neural networks.…”