The emergence, organization, and persistence of cellular life are the result of the functional integration of metabolic and genetic networks. Here, we engineer phospholipid vesicles that can operate three essential functions, namely transcription-translation of a partial genome, self-replication of this DNA program, and membrane synthesis. The synthetic genome encodes six proteins, and its compartmentalized expression produces active liposomes with distinct phenotypes demonstrating successful module integration. Our results reveal that genetic factors exert a stronger control over DNA replication and membrane synthesis than metabolic crosstalk or module co-activity. By showing how genetically encoded functions derived from different species can be integrated in liposome compartments, our work opens avenues for the construction of autonomous and evolving synthetic cells.

In cell-free gene expression, low input DNA concentration severely limits the phenotypic output, which may impair in vitro protein evolution efforts. We address this challenge by developing CADGE, a strategy that is based on clonal isothermal amplification of a linear gene-encoding dsDNA template by the minimal Φ29 replication machinery and in situ transcription-translation. We demonstrate the utility of CADGE in bulk and in clonal liposome microcompartments to boost up the phenotypic output of soluble and membrane-associated proteins, as well as to facilitate the recovery of encapsulated DNA. Moreover, we report that CADGE enables the enrichment of a DNA variant from a mock gene library via either a positive feedback loop-based selection or high-throughput screening. This new biological tool can be implemented for cell-free protein engineering and the construction of a synthetic cell.

The reconstitution of basic cellular functions in micrometer-sized liposomes has led to a surge of interest in the construction of synthetic cells. Microscopy and flow cytometry are powerful tools for characterizing biological processes in liposomes with fluorescence readouts. However, applying each method separately leads to a compromise between information-rich imaging by microscopy and statistical population analysis by flow cytometry. To address this shortcoming, we here introduce imaging flow cytometry (IFC) for high-throughput, microscopy-based screening of gene-expressing liposomes in laminar flow. We developed a comprehensive pipeline and analysis toolset based on a commercial IFC instrument and software. About 60 thousands of liposome events were collected per run starting from one microliter of the stock liposome solution. Robust population statistics from individual liposome images was performed based on fluorescence and morphological parameters. This allowed us to quantify complex phenotypes covering a wide range of liposomal states that are relevant for building a synthetic cell. The general applicability, current workflow limitations, and future prospects of IFC in synthetic cell research are finally discussed.

Cell-free transcription and translation systems promise to accelerate and simplify the engineering of proteins, biological circuits and metabolic pathways. Their encapsulation on microfluidic platforms can generate millions of cell-free reactions in picoliter volume droplets. However, current methods struggle to create DNA diversity between droplets while also reaching sufficient protein expression levels. In particular, efficient multi-gene expression has remained elusive. We here demonstrate that co-encapsulation of DNA-coated beads with a defined cell-free system allows high protein expression while also supporting genetic diversity between individual droplets. We optimize DNA loading on commercially available microbeads through direct binding as well as through the sequential coupling of up to three genes via a solid-phase Golden Gate assembly or BxB1 integrase-based recombineering. Encapsulation with an off-the-shelf microfluidics device allows for single or multiple protein expression from a single DNA-coated bead per 14 pL droplet. We envision that this approach will help to scale up and parallelize the rapid prototyping of more complex biological systems.