Machine Learning enabled Pooled Optical Screening in Human Lung Cancer Cells
Abstract
Pooled CRISPR-based gene knockout (KO) screening has emerged as a powerfulmethod to uncover gene effects on various phenotypes [1, 2].
Recently, an opticalpooled CRISPR screening method was developed [3] in which gene targetingguide-RNA (gRNA) are determined using in situ sequencing coupled with microscopy imaging of cellular structure and spatial features [3–6].
Pooled opticalscreening is very scalable and cost-effective.
It can be coupled with differentimaging assays to perform large-scale high-content image-based CRISPR-based
KO screens.
However, development of automated and general approaches for dataprocessing and analysis are required to unlock its full potential as a tool for drugtarget discovery.
Here, we introduce a machine-learning enabled computationalframework for in situ sequencing, segmentation and feature representations of cellmorphology from pooled optical screens and apply it to human lung cancer cells(A549).
We develop a convolutional neural network (CNN) method for gRNAsequence calling, and show that it increases the cell yield by 10% and enablesautomation.
We suggest self-supervised single-cell embeddings as a method tocreate informative representations of cell morphology, moderately improving uponcommonly used engineered features.
We demonstrate that such embeddings, aggregated for each gene KO, are more similar for gene pairs that are known to interactand cluster genetic perturbations by their cellular components, biological pathways,and molecular functions.
We also highlight ways to use the perturbation clusters togenerate hypotheses about gene functions, which are consistent with results fromorthogonal studies.
Put together, we develop a scalable and general computationalapproach to process and analyze pooled CRISPR-based morphological screens thatcan be applied to screen for various disease relevant phenotypes.
Introduction
Pooled CRISPR KO screening technologies have been widely used for conducting large scaleinvestigation of gene effects on diverse sets of phenotypes.
Recently, Feldman et al. introduced amethodology for performing optical pooled screens in human cells [3, 7] by obtaining high-contentimage-based data with their corresponding perturbation identities from pooled CRISPR screens.
Briefly, this approach involves transfection of a pool of cells with gRNAs to enable targeted CRISPRediting.
Cells are then run through a phenotyping assay such as antibody staining and fixed.
ThegRNAs within the cells are then amplified using rolling circle amplification, and in situ sequencing isthen conducted on the plates to read out the gRNA and respective CRISPR knockout within each cell.
This approach was applied to study the NFkB pathway using p65 protein localization as a readout [3],and in a later work to study essential genes using intensity features derived from fluorescence markers[6], but not yet to a general morphology assay.
Cell Painting [8] is a morphology imaging techniquethat is known to contain rich information about cell state, allowing practitioners to cluster compoundsby their MOA.
However, Cell Painting phenotypic screening is typically performed in arrayed formatwhich is costly, labor intensive and is subject to batch effects [9].
Combining pooled optical screens with a general morphological profiling assay such as Cell Painting can provide an efficient and generalassay for morphological screening in large genetic perturbation screens.
Processing pooled opticalscreening data is challenging.
It requires accurate gRNA sequencing, accurate segmentation of cellextents and correct association of guides to target-cells.
In this work, we describe a computationalframework for analyzing a screen that combines an adapted form of Cell Painting (high-content) witha pooled optical screen (high-throughput).
In the following sections we present machine learningenabled methodologies for in situ sequencing and self-supervised feature extraction followed bythe construction of a gene-gene phenotype similarity network.
We demonstrate and evaluate theapplication of the above methodologies in learning gene similarity networks from a 300 gene (4gRNAs per gene) pooled CRISPR knock-out screen dataset containing ∼ 1 . 5 million cells.
Machine learning improves in situ sequencing
To scale up pooled optical screening we developed a fully automated pipeline for processing (Figure1A).
Hereinbelow are some of the method improvements that enabled this pipeline.
During the in situ sequencing step, each plate is processed to amplify the gRNA sequence presentin each cell.
These gRNAs are sequenced by synthesis (SBS) by labeling each nucleotide with aunique fluorophore, stripping, then relabeling with the next nucleotide in the sequence in a cyclicmanner.
This leads to a dataset in which the full plate is imaged several times, with stationary dotsshowing variable fluorescent signatures that need to be converted to sequencing base calls.
A majorstep in optical screens is in situ sequencing of the gRNA.
Feldman et.
al [3], presents a computationalmethodology for in situ gRNA sequencing that requires manual alignment of field-of-view imagesduring acquisition followed by local image registration and blob detection that requires manual finetuning of parameters.
Here, we propose an improved methodology for base-calling by training a3-layer fully convolutional neural network that takes as input a sequencing-by-synthesis fluorescencebase call image with channels corresponding to fluorescent nucleotide signals (A, C, T, G) andproduces a probability mask corresponding to each channel (Figure 1B).
We then use the probabilitymask to identify base locations and the corresponding base call.
The base calls are stitched based onspatial correspondence across all the SBS acquisition cycles to generate a gRNA barcode readoutcorresponding to each spatial location in the image (the first k (k=10) bases of the gRNA isreferred to as a barcode in subsequent sections).
Our method does not require manual alignment offield-of-view images at acquisition time, does not require manual parameter tuning, and increases thepercentage of cells recovered with valid gRNA barcode from 68.6% to 78.79% in our test dataset(Table 1).
[Table]
The gRNA barcode locations computed from the above step are projected onto the Cell Paintingfluorescence images using a coordinate transformation matrix constructed by image registrationbetween the acquisitions.
The Cell Painting images are then preprocessed to correct for illuminationand intensity artifacts and single cell and nuclei contexts are segmented using CellPose [10].
Finally,a single cell dataset is generated by cropping tiles centered on each nucleus and masked by itscorresponding cell mask.
Each tile is associated with a gRNA identity based on the mapped barcodelocations.
Self-supervised models generate biologically informative embeddings
High-content image-based screens using Cell Painting have been shown to be useful in learningrepresentations and morphological profiling of perturbation effects in cells [9, 11].
Funk et.
al [6]demonstrated that simple fluorescence intensity and cell shape features derived from pooled opticalscreens can be useful in defining the functional landscape of human essential genes.
While explicitfeatures such as intensity and shape features can be useful, they need to be manually engineered and donot capture all the kinds of variation that can occur in a perturbation dataset.
Self-supervised learningmethods [12, 13] have been shown to improve the quality of learned representations compared tosupervised learning methods.
Recently, SimCLR [12] and DINO-ViT [13] have achieved state-of-theart performance in learning representations from natural images.
Here, we utilize these frameworks tolearn single-cell phenotype representations that can be used to create gene-gene phenotype similaritynetworks.
The process is as follows: 1) we extract single-cell representations 2) represent each geneticperturbation by the median over all cells of that gene perturbation.
3) reduce the dimensionalityof these to the top 200 principal components. 4) form a correlation matrix 5) threshold to keepsignificant correlations 6) cluster with a community detection method (Leiden).
To assess the performance of the methodology in learning biologically meaningful feature representations, we obtained evaluation metrics based on the overlap of our learned gene-gene phenotypenetwork with publicly available gene-network and ontology (STRING DB and Gene Ontology)databases.
For STRING DB [14] evaluation, we used the protein-protein interaction network (combined_score > 900 as positive interaction, combined_score = 0 as no evidence of interaction) datasetas the ground truth dataset.
For gene ontology evaluation, we constructed a ground truth networkby adding an edge between a pair of genes if they belong to a common gene ontology (GO) set ineach of the datasets (Cellular Component, Biological Process and Molecular Function) as obtainedfrom MSigDB [15] (gene sets containing > 25 genes (out of the 300 genes in the screen) werenot considered for evaluation).
For each of the above ground truth datasets, we computed the areaunder the receiver operating characteristic curve (AUC) of the overlap between the latent spacecorrelation matrix and the ground truth gene network.
We trained and evaluated self-supervisedmodels using the evaluation metric on the 300-gene perturbation dataset: 1.
SimCLR model trainedusing a resnet-50 backbone, 2.
DINO model trained with vision transformer backbone (vit-small,patch_size=16) (DINO-ViT), 3.
DINO-ViT with positive pairs sampled from samples having thesame gene perturbation, 4.
DINO-ViT with positive pairs sampled from samples having the samegRNA barcode, 5.
DINO-ViT model trained on ImageNet pretrained weights and fine-tuned withpositive pairs sampled from samples having the same gRNA barcode.
We also compare the abovemodels against an ImageNet pretrained DINO-ViT model and explicitly engineered cell intensity andmorphology features as baselines.
The results show that pre trained or fine tuned DINO-ViT featuresoutperform the commonly used engineered features (Figure 2, Table A1).
methodologies based on their ability to represent known gene-gene relationships as measured by area under the receiver operating characteristic curve (AUC)
The DINO-ViT representation of the information-rich Cell Painting assay allows reconstruction ofgene networks, and identification of new pathway components in a hypothesis-free way.
Specifically,we were able to reconstruct the genetic modifiers of PI3K/Akt activation, protein glycosylation, fattyacid biosynthesis, sterol regulatory pathway, mitochondrial-inner membrane genes and more (Figure3).
Interestingly, the network was capable of clustering key components of the lipogenesis pathways.
Namely, the core fatty acid synthesis enzymes (ACLY: ATP Citrate Lyase, ACACA: Acetyl-CoA
Carboxylase Alpha, and FASN: Fatty Acid Synthase), upstream AKT signaling regulators (PIK3R3:
PI3K Regulatory Subunit 3, PIK3R4: PI3K Regulatory Subunit 4), and downstream palmitate andmevalonate pathway regulators (SCD: Stearoyl-CoA Desaturase, SREBF1: Sterol Regulatory Element
Binding Transcription Factor 1) all contribute to lipogenesis [16]; our screening and ML approachwas capable of grouping these regulators in an unsupervised manner using a generic morphologicalreadout.
While typical CRISPR screens would be capable of finding genes that increase or decreaselipogenesis, this model appears to have achieved a higher level of granularity by producing thesesub-clusters.
Another striking observation is the clustering of CDK5 with genes from the mTORpathway (RHEB, mTOR and PDPK1; Figure 3, bottom left).
CDK5 was recently identified tophosphorylate S6 [17], our result supports its role as an mTOR pathway regulator.
In contrast to that
NRAS, which is closely related to the KRAS, has a distinct morphological phenotype, different fromthe KRAS cluster (KRAS, BRAF and EGFR; Figure 3, top right).
This observation may reflect thedifferent function of these Ras isoforms [18].
In summary, streamlined data processing pipelines and in situ sequencing methods via automationallow for increased scale, gRNA coverage, and improved gene network reconstructions.
Thistechnique opens the possibility of whole-genome screening for numerous imaging-based phenotypesin a variety of cellular models.
A Appendix
[Table] [Table]