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Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations.

Cell size is believed to influence cell growth and metabolism. Consistently, several studies have revealed that large cells have lower mass accumulation rates per unit mass (i.e. growth efficiency) than intermediate sized cells in the same population. Size-dependent growth is commonly attributed to transport limitations, such as increased diffusion timescales and decreased surface-to-volume ratio. However, separating cell size and cell cycle dependent growth is challenging. To decouple and quantify cell size and cell cycle dependent growth effects we monitor growth efficiency of freely proliferating and cycling polyploid mouse lymphocytes with high resolution. To achieve this, we develop large-channel suspended microchannel resonators that allow us to monitor mass of single cells ranging from 40 pg (small diploid lymphocyte) to over 4000 pg, with a resolution ranging from ~1% to ~0.05%. We find that mass increases exponentially with respect to time in early cell cycle but transitions to linear dependence during late S and G2 stages. This growth behavior repeats with every endomitotic cycle as cells grow in to polyploidy. Overall, growth efficiency changes 29% due to cell cycle. In contrast, growth efficiency did not change due to cell size over a 100-fold increase in cell mass during polyploidization. Consistently, growth efficiency remained constant when cell cycle was arrested in G2. Thus, cell cycle is a primary determinant of growth efficiency and increasing cell size does not impose transport limitations that decrease growth efficiency in cultured mammalian cells.Significance statementCell size is believed to influence cell behavior through limited transport efficiency in larger cells, which could decrease the growth rate of large cells. However, this has not been experimentally investigated due to a lack of non-invasive, high-precision growth quantification methods suitable for measuring large cells. Here, we have engineered large versions of microfluidic mass sensors called suspended microchannel resonators in order to study the growth of single mammalian cells that range 100-fold in mass. This revealed that the absolute size of a cell does not impose strict transport or other limitations that would inhibit growth. In contrast to cell size, however, cell cycle has a relatively large influence on growth and our measurements allow us to decouple and quantify the growth effects caused by cell cycle and cell size.

We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-of-principle, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts.