Experimental single-cell data often presents an incomplete picture due to its destructive nature: 1) we collect certain experimental measurements of cells but lack measurements under different experimental conditions or data modalities; 2) we collect data of cells at certain time points but lack measurements at other time points; or 3) we collect data of cells under certain perturbations but lack data for other types of perturbations. In this article, we will discuss machine learning approaches to address these types of translation and counterfactual problems. We will begin by giving an overview on single-cell biology applications and the relevant translation problems. Subsequently, we will provide an overview of approaches for multidomain alignment and translation in machine learning, including methods based on generative modeling, optimal transport, and causal inference. The bulk of this article will focus on how these approaches have been tailored and applied to important translation problems in single-cell biology, illustrated through concrete examples from our own work. We end with open problems and a perspective on how biology may not only be uniquely suited to being one of the greatest beneficiaries of machine learning but also one of the greatest sources of inspiration for it.

Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used.