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Modular deep learning enables automated identification of monoclonal cell lines

A preprint version of the article is available at bioRxiv.


Monoclonalization refers to the isolation and expansion of a single cell derived from a cultured population. This is a valuable step in cell culture that serves to minimize a cell line’s technical variability downstream of cell-altering events, such as reprogramming or gene editing, as well as for processes such as monoclonal antibody development. However, traditional methods for verifying clonality do not scale well, posing a critical obstacle to studies involving large cohorts. Without automated, standardized methods for assessing clonality post hoc, methods involving monoclonalization cannot be reliably upscaled without exacerbating the technical variability of cell lines. Here, we report the design of a deep learning workflow that automatically detects colony presence and identifies clonality from cellular imaging. The workflow, termed Monoqlo, integrates multiple convolutional neural networks and, critically, leverages the chronological directionality of the cell-culturing process. Our algorithm design provides a fully scalable, highly interpretable framework that is capable of analysing industrial data volumes in under an hour using commodity hardware. We focus here on monoclonalization of human induced pluripotent stem cells, but our method is generalizable. Monoqlo standardizes the monoclonalization process, enabling colony selection protocols to be infinitely upscaled while minimizing technical variability.

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Fig. 1: Summary of the four CNN ‘modules’ used in Monoqlo.
Fig. 2: Overview of the daily automation workflow that generates data for training and real-time use with Monoqlo.
Fig. 3: Subset of daily scans of an example iPSC colony growing in culture, which was confirmed as monoclonal by manual image review.
Fig. 4: Overview of Monoqlo’s design and algorithmic logic.
Fig. 5: Results of Monoqlo framework validations.

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Data availability

All images from DMR0001, the full monoclonalization run used in the validation of Monoqlo during this study, are available for download from

Code availability

The Python code base for executing the Monoqlo framework is available for download at For a direct link to the code only, see (


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This work was supported by The New York Stem Cell Foundation (NYSCF). We thank the members of the NYSCF leadership team, specifically R. Monsma, S. Noggle, R. Aiyar, C. Anzel, L. Schwarzbach, J. Wallerstein and S. Solomon, for their support throughout this work. We also thank L. Mehran and M. Berliss for their guidance on reporting of biological research protocols. We thank C. Richardson for his hugely helpful guidance on the release of Monoqlo.

Author information

Authors and Affiliations




B.F., D.P. and Z.W. conceptualized the Monoqlo framework, including the use of reverse chronological analysis for the assessment of clonality. B.F. trained and validated RetinaNet detection models and wrote the Python software for the execution and automated deployment of Monoqlo, including data-handling logic, image processing and integration of deep learning models. B.F. conceptualized the use of classification networks in automatically assigning morphological classifications to the most recent colony images. B.F., S.H., B.H., D.P. and J.B. conceptualized the labelling system for classifications of colony morphology. S.H. labelled training data and trained and validated all morphology classification models. G.L. and D.P. developed NYSCF’s iPSC monoclonalization laboratory-automation and colony-selection protocols. B.F., B.H., J.B., D.P. and NYSCF Global Stem Cell Array Team performed image annotations for training the RetinaNet models. D.H., B.H., M.Z., J.B. and NYSCF Global Stem Cell Array Team performed physical monoclonalizations, validation of the Monoqlo framework and subsequent cell culture and imaging using robotic systems.

Corresponding authors

Correspondence to Brodie Fischbacher or Daniel Paull.

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Competing interests

B.F., Z.W. and D.P. are co-inventors on a pending patent regarding an image system and method of use (pub. no. WO2021067797A1). The authors declare no other competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Santiago Miriuka, Lassi Paavolainen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Examples of each morphological class used in training Monoqlo’s classification CNN module.

M1 is the desired morphology, indicating a healthy, pluripotent stem cell colony, and is defined as having a clearly defined, tight perimeter, round shape, no evidence of differentiation and a core with a smooth, transparent appearance. M2 is defined as a colony with the morphology of M1 but with a differentiated fringe. In the displayed example, differentiation and thus loss of pluripotency is clearly shown by the spindle-shaped cell formations and round core with a dark coloration in the bottom left of the tile. M3 is defined as a colony with a poorly-defined shape and often a predominantly dark coloration, which can indicate either differentiation or a dense aggregation of dead cells. M4 is a fully differentiated colony, composed entirely of sprawling, spindle-shaped cell aggregations, and displaying none of the desired morphological markers of pluripotency or iPSC health status.

Extended Data Fig. 2 Example of poor performance by a generalized model trained across all functionalities.

In this instance, the colony detection Is correct. However, the cell detection, in addition to being incorrect, is impossible at the given image magnification and time point.

Extended Data Fig. 3 Predicted Colony Width vs Ground Truth.

Relationship between width of colony bounding box predicted by Monoqlo’s global detection model and the true width measured by biologists with a scale bar image overlay, plotted using 268 measurement-prediction pairs.

Extended Data Fig. 4 Example of abiotic artifacts causing false colony detections by Monoqlo’s global detection model.

a) and b) represent the same image report by Monoqlo, full view and zoomed, respectively.

Extended Data Fig. 5 Example gating strategy.

Representative gating strategy employed during FACS-sort monoclonalization of iPSCs.

Extended Data Fig. 6 Overspill labelling example.

Labelling example in which an additional object class, ‘overspill’ (indicated by blue bounding boxes,) is annotated to improve model performance and mitigate erroneous detections of the ‘colony’ (green bounding box) object class.

Extended Data Fig. 7 Model training and selection.

a, Training and validation accuracy trajectories of the classification CNN, plotted against epoch. Red and green dots signify training and validation accuracies, respectively. b, Confusion matrix of fully trained classification CNN when validated on held-out test set. Scale bar indicates color shading key, indicating the number of examples classified for respective classes as a proportion of total number of examples for the given class. c, Example training and validation accuracy over train time of the RetinaNet detection CNN.

Extended Data Fig. 8 Overlapping detections.

Example of overlapping reports of colonies by Monoqlo’s local detection model where only a single colony exists after ground-truthing.

Extended Data Fig. 9 Colony splitting example.

Illustration of the concept of “colony splitting’, where an apparent single colony is revealed, during reverse-chronological analysis, to have originated in multiple colonies which ultimately merged.

Supplementary information

Supplementary Information

Full details on example neural network architectures from the Monoqlo framework.

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Fischbacher, B., Hedaya, S., Hartley, B.J. et al. Modular deep learning enables automated identification of monoclonal cell lines. Nat Mach Intell 3, 632–640 (2021).

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