A synthetic cell with integrated DNA self-replication and lipid biosynthesis

Domain: neuroscience
Published: 2026-02-13
Editorial outcome: accept

Abstract
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.

Introduction
The construction of a synthetic cell from the bottom up is a grand challenge at the intersection of bioscience and engineering. Inspired by the observation of common processes in all living organisms, researchers have started to build some of the essential cellular functions, hereafter called ‘modules’, in vitro. The expanding repertoire of genetic parts and characterized biochemical networks has enabled the cell-free reconstitution of life’s fundamental mechanisms, such as the synthesis of membrane constituents1–3, division related processes4,5, DNA replication6,7, energy regeneration8, and cell–cell communication9. While these studies have yielded valuable insights into the specifics of each biological module, they have not addressed the higher-ordered complexity that lies in the integration of multiple processes, in particular when the involved genetic or protein parts are derived from various organisms10,11.

Three subsystems appears essential for basic cellular life: a vesicular system defining an internal machinery that synthesizes its membrane constituents, a replicable template that carries information, and a metabolic cycle that produces the molecular components12,13. As a construction paradigm, we envisioned that in vitro transcription-translation (IVTT) of a synthetic DNA template using recombinant elements (PURE system)14 inside phospholipid vesicles (liposomes) would constitute the scaffold onto which biological functions can be implemented to create an autonomously living synthetic cell. In contrast to other approaches which rely exclusively on purified proteins or cell lysates, our DNA-based architecture enables replication, system’s level evolution, and is constituted of well-defined components11. Replication of the DNA program can be seen as the seed module priming self-maintenance and evolvability15. However, an experimental demonstration of module integration directed by a synthetic self-replicating genome has remained elusive.

Here, we pin the work for combining synthetic cell modules by constructing a self-replicating DNA genome, named DNArep-PLsyn, encoding both a DNA replication machinery (DNArep) and a phospholipid biosynthesis pathway (PLsyn). We establish the conditions for in-liposome expression of DNArep-PLsyn with PURE system and demonstrate the combined activities of universal cellular modules in a minimal in vitro system.

Results
We constructed a synthetic DNA replication system following the design of the Փ29 genome16 which consists of a linear DNA template with origin of replication sequences at each end. Previous work showed that four phage proteins—the terminal protein (TP) that functions as a replication primer, the DNA polymerase (DNAP), the single-stranded DNA binding protein (SSB), and the double-stranded DNA binding protein (DSB)—were sufficient to replicate a linear DNA in vitro17. Moreover, we previously showed that expression in PURE system of a minimal Փ29-based linear replicon encoding DNAP (p2 gene) and TP (p3 gene) led to exponential amplification of DNA, also when the reaction was compartmentalized inside micrometer-sized liposomes7,15. We here sought to integrate additional genes into this seed replication module and hypothesized that the larger synthetic genome could be replicated—and all the gene products could be synthesized—upon expression in PURE system (Fig. 1a–d). The newly introduced genes encode four enzymes of the E. coli Kennedy pathway: sn-Glycerol-3-phosphate acyltransferase (PlsB), Lysophosphatidic acid acyltransferase (PlsC), Phosphatidate cytidylyltransferase (CdsA), and Phosphatidylserine synthase (PssA) (Fig. 1d). These enzymes catalyze the sequential conversion of oleoyl-CoA and glycerol-3-phosphate precursors into 1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPS), the last intermediate for 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) production. Membrane synthesising gene-expressing vesicles can then be visualized using a PS-specific fluorescent probe2. Therefore, our final linear genome, named DNArep-PLsyn, is flanked with Փ29 origins of replication on each end, and it encompasses six genes (two for DNArep and four for PLsyn) as individual transcription units (Fig. 1a).

To construct the DNArep-PLsyn synthetic genome, we iterated throughout different cloning strategies and found that template complexity (i.e., repetitive elements) often led to recombination events in E. coli. We then opted for an in vitro DNA assembly approach using overlapping polymerase chain reaction (PCR) to stitch the DNArep and PLsyn genetic parts (Supplementary Fig. 1), and a yeast-based cloning approach (Supplementary Fig. 2). Notably, S. cerevisiae yeast did not seem to pose recombination issues with repetitive regulatory sequences, unlike E. coli. After plasmid extraction from yeast, we generated the linear DNArep-PLsyn genome by PCR. In both in vitro and in-yeast DNA assemblies, we successfully obtained a linear template with the expected size (∼9600 bp), and the sequence was validated by nanopore sequencing (Supplementary Fig. 1 and 2).

Next, we confirmed that the expression of DNArep-PLsyn with PURE system generates the six encoded proteins. The reaction mix was supplemented with GreenLys reagent for co-translational protein labeling. All six proteins were produced at detectable levels starting from DNA assembled in vitro or in yeast (Fig. 1e, Supplementary Fig. 3 and 4). Interestingly, only a slight reduction of protein expression levels was observed for the DNArep-PLsyn template compared to the separate expression of each individual genetic module (Fig. 1e and Supplementary Fig. 3). This could be caused by resource sharing when the number of genes increases, but the effect was less pronounced than expected. We conclude that DNArep-PLsyn acts as an effective template for expressing all necessary proteins involved in both DNArep and PLsyn modules.

Our next aim was to evaluate and potentially optimize the simultaneous activity of the DNArep and PLsyn modules inside liposomes. Since each of the encoded modules may have a preferred reaction temperature (DNA replication works well at ∼30 °C7,17, while cell-free gene expression18 and phospholipid biosynthesis2 are most effective at 37 °C), we decided to test different incubation temperatures. We encapsulated in liposomes the DNArep-PLsyn genome together with PURE system and the required substrates and cofactors for both DNArep and Plsyn, and we ran the reactions at 30 °C, 34 °C, or 37 °C. After overnight incubation, we stained the DNA with the dsGreen intercalating dye19 and the membrane-incorporated DOPS with the PS-specific probe LactC2-mCherry2, and we analyzed the samples by flow cytometry (Fig. 2a–d). For each fluorescent probe, we performed an intensity thresholding based on negative control samples (Supplementary Fig. 5), thus defining four regions of interest (ROI) in the scatter plot (Fig. 2d). Liposomes exhibiting functional DNArep (ROI 1+ROI 2) or PLsyn (ROI 2+ROI 4) modules were detected at all three temperatures (Fig. 2a, b), with a slightly higher occurrence for DNArep-active liposomes at 34 °C than at 30 and 37 °C (Fig. 2b–d). Notably, a range of 0.4 to 12% of the liposomes (corresponding to ∼50–1200 liposomes per sample across biological replicates at 34 °C) localized in ROI 2 indicating that both DNArep and PLsyn modules were simultaneously active (Fig. 2c, d, and Supplementary Fig. 5). A larger fraction of liposomes was positive to either one of the two modules (ROI 1 or ROI 4) or was inactive (ROI 3) (Fig. 2a, b, d). Such a heterogeneity within the same clonal (here referring to the fact that one DNA species was used) population of liposomes is also observed in single-gene expression experiments and can be attributed to uneven loading or supply of substrates or cofactors, or to varying expression levels of the genetic modules between liposomes20. In addition, a significant variability across biological replicates (sample-to-sample heterogeneity) was observed. For example, the percentage of DNArep-PLsyn-positive liposomes (ROI 2) was 2.9 ± 1.3% (mean ± s.e.m) across eight biological replicates at 34 °C (Supplementary Fig. 5). Nonetheless, these results demonstrate that functional integration of DNArep and PLsyn modules from a synthetic genome is possible at temperatures ranging from 30 °C to 37 °C.

To provide more direct evidence of genome self-replication, we measured the concentration of DNA using quantitative PCR (qPCR). Two different sequences localized in opposite regions of the linear DNArep-PLsyn genome were targeted for qPCR, one in the p2 gene and one in the pssA gene. The results quantitatively confirmed that all tested temperatures supported genome replication, again with a slight preference for 34 °C (Fig. 2e, f). We further investigated whether the full-length genome was amplified (vs. shorter amplicons) by targeting all six genes by qPCR. These experiments were performed at 34 °C. Despite some variations in the concentration of replicated genes, the data showed that the entire DNA sequence between the p3 and pssA genes (∼5000 bp apart) was amplified about 10-fold (Supplementary Fig. 6). Small differences could arise from DNA replication arrest events, leading to incomplete fragment amplification21, or from qPCR-related variations in the gene-specific primer design and efficiency. Since qPCR amplifies only ∼200-bp regions and the terminal origins of replication were not targeted, we also recovered DNA from liposome samples by PCR followed by agarose gel analysis of the amplification products. The entire DNArep-PLsyn genome (within the resolution of agarose gel electrophoresis) could be recovered from diluted liposome samples (Supplementary Figs. 6 and 7). Shorter DNA species were also observed, suggesting that the DNArep-PLsyn genome may have experienced incomplete self-replication or that smaller DNA fragments were generated during PCR recovery.

Next, we sought to directly demonstrate the production of PS and intermediate enzymatic products of the reconstituted phospholipid biosynthesis pathway by liquid chromatography-mass spectrometry (LC-MS). We found that DOPS was produced, although not in high concentrations (Fig. 2g and Supplementary Fig. 8). Moreover, DOPA was accumulated, suggesting that CdsA may be limiting the yield of PS production. Considering that dioleoyl-phosphatidylglycerol (DOPG) accounts for 12% of the total lipids, we estimated that synthesized DOPS would represent 0.7% of the total lipid content after 16 h incubation at 34 °C. However, it is relevant to note that LC-MS gives ensemble measurements, the obtained concentration values reflecting the average activity of all the liposomes in the sample. Individual vesicles may contain none or higher-than-average amounts of DOPS (see next section).

Having established the successful integration of the DNArep and PLsyn modules, we aimed to directly visualize the different liposome phenotypes, allowing for a more accurate classification based on activity levels. In particular, we asked whether liposome size, lamellarity, or morphology could affect or be affected by module activity. We combined fluorescence confocal microscopy with an in-house-developed software called SMELDit to enable automated liposome identification, feature analysis, and image recovery from scattered data plots (see Methods). We expressed DNArep-PLsyn in liposomes at 34 °C and used the dsGreen and LactC2-mCherry signals as fluorescent markers for DNArep and PLsyn activity, respectively (Fig. 3a). We observed that the addition of the substrates and cofactors, and the expression of DNArep-PLsyn did not affect liposome sample quality (Fig. 3a, Supplementary Fig. 9, and Supplementary Movie 1). Notably, images unraveled phenotypic traits that could not be inferred from flow cytometry data, such as the presence of bright dsGreen spots in the vesicle lumen, which result from active DNA replication. We had already observed a similar phenotype during amplification of a shorter DNA self-replicator, which was attributed to an induced condensation of highly concentrated DNA7,22. Here, it is interesting to see that such a phenomenon is also possible with a 3-fold longer DNA template (∼9.6 kb vs. ∼3.2 kb) containing more expressed genes (6 vs. 2).

When aggregating data from all biological replicates, over 34,000 liposomes were recognized. We generated a phenotype map corresponding to the two-dimensional plot of LactC2-mCherry vs. dsGreen signals from single vesicles (Fig. 3b). Liposomes were classified according to four different phenotypes based on intensity thresholding, akin to flow cytometry data analysis (ROI 1-4) (Fig. 2d). We found that ∼8% of liposomes, corresponding to over 2900 liposomes, had coexisting DNA replication and DOPS synthesis (ROI 2). Vesicles with either active PLsyn (∼10%, ROI 4) or active DNArep (∼31%, ROI 1) module were more abundant (Fig. 3b). We then questioned whether liposome sizes varied across the four regions, for example as a result of membrane synthesis. Vesicle size distribution was computed for each phenotypic region (Supplementary Fig. 10). We observed no marked differences in the median values of the apparent diameter between active (ROI 2 and 4) and inactive (ROI 1 and 3) PLsyn module (3.7 ± 2.4 µm median across all ROIs), indicating that the yield of newly synthesized lipids is not sufficient for detectable physical growth of liposomes.

To account for the variability across biological replicates, we constructed the phenotype map for each replicate sample (Fig. 3c and Supplementary Fig. 11). Despite clear variations in the percentages of gated liposomes in each region, all replicates contained vesicles exhibiting simultaneous DNArep and PLsyn activity (Fig. 3c). Finally, we examined the LactC2-mCherry and dsGreen intensity values for every liposome as this may reveal differences in the efficacy of a given module when operating alone or together. From both pooled data and individual replicates, we observed no strong differences in the intensity pattern of the module activity reporter dyes between ROIs (Fig. 3d). This result suggests that DNArep activity is not lessened when coupled with PLsyn activity, and vice-versa. These findings point to a robust compatibility between the two functions. In addition, some liposomes exhibit an intensity of the DOPS probe that can be over one order of magnitude higher than the average value (Fig. 3d), indicating that synthesized DOPS could represent up to 7% (0.7 × 10) of the total lipid content.

For better generalization of image analysis across experiments and sample types, we developed a deep-learning-based method for automated vesicle segmentation and phenotype classification. The model was able to identify 306 liposomes with 90% confidence out of 353 manually identified vesicles (Supplementary Fig. 13). Moreover, the model enabled classification of the phenotypes, including vesicles with localized replication foci, without relying on fluorescence intensity thresholding or other predefined parameters (Supplementary Fig. 14).

To better understand the influence that active DNArep or PLsyn modules may have on each other, we assayed liposomes expressing the full synthetic genome, this time by adding either of the two sets of substrates/cofactors (DNArep or PLsyn) (Fig. 4a). An additional condition was tested, where all DNArep substrates/cofactors were supplied, except for dNTPs. This switches DNArep module OFF but allows to study the effect of the other molecules (i.e., SSB, DSB, ammonium sulfate). We reasoned that possible inhibitory effects may arise by the substrates themselves, intermediate reaction products (e.g., lysophosphatidic acid, DOPA), or byproducts (e.g., Coenzyme A, deoxynucleoside monophosphate). Moreover, we hypothesized that DNA processing by the Փ29 DNA polymerase may either have a beneficial effect on PS synthesis by increasing the yield of synthesized enzymes through genome amplification19 or have an adverse effect by hindering gene expression through collision events between DNA-interacting proteins (DSB or DNA polymerase vs. RNA polymerase)21.

Following the same protocol as described above, we verified that DNArep and PLsyn were only active when their corresponding substrates/cofactors were present (Fig. 4b, c, and Supplementary Fig. 14), confirming that nonspecific staining with dsGreen and LactC2-mCherry was negligible. Using data pooled from all biological replicates, we found that the occurrence of DNArep-active liposomes (ROI 1+2) decreased only from ∼38% to ∼31% when PLsyn substrates were supplied, while the occurrence of PLsyn-active liposomes (ROI 2+4) reduced only from ∼18% to ∼15%/∼10% (with/out dNTPs) when DNArep substrates/cofactors were supplemented (Fig. 4d and Supplementary Fig. 15). By examining individual replicates, we found a higher variability on the occurrence of PLsyn-active liposomes when reactions contained all DNA replication substrates/cofactors (both modules ON) compared to in their absence (Supplementary Fig. 15), but its cause remains to be explained. Moreover, the intensity distributions of DNArep and PLsyn activity reporters were similar regardless of the presence or absence of the substrates from the other module (Fig. 4e). Furthermore, DNA replication efficiency was similar with or without the substrates for PLsyn (Fig. 2f and Supplementary Fig. 16). Overall, we conclude that functional integration of the DNArep and PLsyn modules is minimally affected by metabolic crosstalk or by module co-activity.

Next, we investigated whether the genetic background could influence the activity of a module. For this, we compared liposome populations with DNArep-PLsyn genome against liposomes with DNA templates carrying only the genes of a single module, i.e., either DNArep or PLsyn, in the presence of the full set of substrates and cofactors (Fig. 5a). We hypothesized that module activity from the DNArep-PLsyn genome may be compromised by sharing of resources/machinery allocated to gene expression23, by impaired replication caused by strand switching of polymerizing DNAP, or by collision events between DNA interacting proteins (DNA and RNA polymerases)21. All three effects would become more prominent as the number of genes increases. Alternatively, genome amplification may boost lipid biosynthesis by increasing the concentration of PLsyn enzymes19.

As expected, microscopy images showed that the appearance of liposome phenotypes was directed by the encapsulated DNA program (Fig. 5b, c, and Supplementary Fig. 17). Interestingly, the percentages of DNArep-positive liposomes were similar with and without co-expression of the PLsyn genes, decreasing only from ∼45% (ROI 1) to ∼40% (ROI 1+2) when PLsyn was co-expressed (Fig. 5d). Conversely, the percentages of PLsyn-positive liposomes dropped from ∼38% (ROI 4) to ∼18% (ROI 4+2) when DNArep was co-expressed (Fig. 5d and Supplementary Fig. 18), suggesting that PLsyn activity is more sensitive to genetic background and expression burden than DNArep activity. This effect may also limit dual-module activity in liposomes containing DNArep-PLsyn, explaining the higher prevalence of a single phenotype in PLsyn- and DNArep-containing liposomes (ROI 4, ∼38% on PLsyn and ∼44% on DNArep), compared to those with DNArep-PLsyn displaying both phenotypes (ROI 2, ∼8%) (Fig. 5d).

While the occurrence of liposomes exhibiting an active PLsyn module was influenced by the co-expression of DNArep, we noticed that the intensity distributions reporting the levels of DNArep and PLsyn activity were similar under single- and double-genetic module expression conditions (Fig. 5e). For PLsyn, this suggests that, above a detectable activity threshold, DOPS production yield was not affected by co-expression of DNArep genes. For DNA replication, however, dsGreen signal intensity is proportional to DNA quantity, which accounts for DNA length and amplification fold. Therefore, similar dsGreen intensities from replicated DNArep-PLsyn and DNArep templates suggest that the amplification fold of DNArep-PLsyn is lower than that of DNArep given its larger size (∼9.6 kb vs. ∼3.2 kb). To test this hypothesis, we performed absolute DNA quantitation by qPCR, confirming that DNArep-PLsyn replicates at a lower yield than DNArep (∼10-fold vs. ∼100-fold) (Fig. 5f, g). Considering that the yield of synthesized DNAP and TP does not differ much from the DNArep-PLsyn or DNArep templates (in the absence of module-specific substrates and cofactors) (Fig. 1e and Supplementary Fig. 3), we speculate that the processivity of polymerization by DNAP—and not replication initiation—might be the amplification bottleneck, especially under transcribing conditions. This hypothesis is supported by previous observations that up to a length of 6 kb the rate limiting step is initiation; over 6 kb, the DNA length becomes rate limiting24.

We next examined how phenotype appearance developed in the course of gene expression. Flow cytometry data show that the highest percentage of liposomes with joint phenotypes was reached at a later time compared to liposomes containing the genes of a single module (8 vs. 4 h) (Supplementary Fig. 19-21). Time course analysis of DNA replication by qPCR showed that the maximum amplification fold was reached after 4 h for both the DNArep-PLsyn and DNArep templates (Supplementary Fig. 21). This mostly reflects the DNA replication kinetics in the larger population of PLsyn-inactive liposomes expressing the full genome (ROI 1). These results demonstrate that some genetic factors slow down the dynamics of template replication when the PLsyn module is concurrently active.

Finally, we envisioned that module performance and integration could be enhanced through directed evolution. Evolving DNArep-PLsyn for increased functionality, e.g., a higher yield of synthesized phospholipids or faster appearance of the combined modules, would require a recursive cycle of genome library encapsulation and expression—phenotype interrogation—sorting of liposomes with the desired features—DNA recovery and amplification. We here by streamlined the key experimental steps that are required for laboratory evolution (Supplementary Fig. 22).

First, to facilitate handling of DNA across the different stages, we utilized a yeast-assembled plasmid (pY003 or pY005) as a precursor of the linear DNArep-PLsyn template. A cloned and sequence-verified plasmid provides a more stable template for the fast production of DNA libraries. High amounts of the PCR-amplified genome could be obtained, allowing for nanomolar input concentrations. Its expression in liposomes improved the robustness and efficiency of the system, yielding ∼30% (vs 8%) of fully active synthetic cells and over 37% (vs 17%) with at least one functional module, while reducing variability between replicates (Supplementary Figs. 23–25). The change of phenotype map compared to Fig. 3c (template obtained by overlapping PCR) suggests an increased ability of DNArep-active liposomes to also produce phospholipids. Time-lapse imaging of phenotype development shows that DNArep precedes PLsyn and the activity of both modules reaches a plateau after 6 h (Supplementary Fig. 26).

Second, to establish a tight coupling between genotype and phenotype, we reduced the concentration of DNArep-PLsyn genome down to 50 pM, which corresponds to an expected average copy number of DNA per liposome equal to one19. Under these conditions a significant fraction of liposomes exhibiting combined module activation was still detected (Supplementary Fig. 22). Next, we screened liposomes and sorted those identified in ROI 2 by fluorescence activated cell-sorting (FACS) (∼3000 events). The full-length genome was successfully recovered and amplified by PCR (Supplementary Fig. 22), and could serve as a template to start a new round. These data validate that an entire cycle of evolutionary engineering is feasible for improvement of integrated biological functions within a DNA-driven synthetic cell model.

Methods
Buffers and chemicals
All buffers were prepared with MilliQ grade water with 18.2 MΩ resistivity (Millipore, USA). All chemicals were purchased from Sigma-Aldrich unless indicated otherwise.

DNA construct design
Plasmids G363 and G435 utilized for DNArep-PLsyn genome assembly were derived from previously cloned constructs encoding for PL synthesis enzymes (pGEMM7)2 or for Փ29 DNA replication proteins (G95)7. Plasmid G555 harboring the Փ29 ori-flanked PLsyn gene pathway was constructed by subcloning the four-gene PLsyn fragment from G363 (digested with Xho1 and Nco1 restriction enzymes), into a Փ29 origins flanked vector7, also digested with Xho1 and Nco1. PCR fragments for DNArep-PLsyn genome assembly were prepared from G363 with 5’-phosphorylated 491 ChD and 1302 ChD primers (PLsyn frag), and from G435 with 5’-phosphorylated 492 ChD and 1289 ChD primers (DNArep frag). Linear DNA fragments containing either of the two genetic modules were prepared by PCR using 5’-phosphorylated primers 491 and 492 ChD, and KOD Xtreme Hot Start DNA polymerase. The reaction solution contained (final concentrations) 1× Extreme Buffer (MERCK), 0.02 U/µL KOD DNA polymerase (MERCK), ∼0.3 ng/µL DNA template, 300 nM forward and reverse primers, 0.4 mM of each dNTP, and MilliQ water up to 50 µL final volume. The thermocycler protocol was set to 94 °C for 2 min for polymerase activation, followed by 25–30 cycles of 10 s 98 °C, 20 s 60 to 68 °C, 30–60 s/kb at 68 °C depending on the length of the desired amplicon.

When specified, a negative control genome was used. In addition to the PLsyn module used for the construction of the DNArep-PLsyn genome, plasmid G363 encodes the p2 and p3 genes from Փ29 under the control of SP6 promoters. Plasmid G363 was linearized with primers 1119 ChD and 756 ChD to generate a 9627-bp amplicon capable of expressing PlsC, CdsA, PssA, and a 140-amino acid long truncated PlsB. Without the addition of SP6 RNA polymerase, both modules are inactive. The PCR reaction mix consisted of 1× KOD One Master Mix (TOYOBO), 300 nM of each forward and reverse primers (491 and 492 ChD), and ∼0.1 ng µL–1 of plasmid DNA. The thermocycler protocol consisted of 35 cycles of 10 s at 98 °C and 50 s at 68 °C.

All PCR amplicons were verified for correct DNA length by 0.7-1% agarose gel electrophoresis before PCR clean-up with QIAquick PCR purification kit (Qiagen). Whenever needed, DNA was purified directly from the agarose gel with a QIAquick Gel Extraction Kit (Qiagen). Both PCR and gel purification standard protocols were modified with a longer column drying step (∼5 min at 10,000 g) before DNA elution with MilliQ water. Purified DNAs were quantified by Nanodrop 2000c spectrophotometer (Isogen Life Science).

In vitro assembly of the DNArep-PLsyn genome
DNArep-PLsyn genome was constructed by overlap PCR from DNArep and PLsyn fragments with two main PCR steps: (i) plasmid overlap and full product DNA extension, and (ii) addition of 491 and 492 ChD primers and PCR amplification of the full DNArep-PLsyn product. 47 µL reactions were prepared with final concentrations of 1× Extreme Buffer (MERCK), 0.02 U/µL KOD DNA polymerase (MERCK), ∼0.3 ng µL–1 DNA template, 0.4 mM of each dNTP, and MilliQ water. The thermocycler was programmed for 2 min at 94 °C for polymerase activation, followed by five cycles of (10 s at 98 °C, 20 s at 60 °C and 3 min and 40 s at 68 °C), and 20 cycles of (10 s at 98 °C, 7 min at 68 °C). For the last 20 cycles of the overlap PCR program, primers 491 and 492 ChD were added to the reaction to a final concentration of 300 nM each. The list of primers is reported in Supplementary Table 2.

In vivo assembly of DNArep-PLsyn in yeast
In vivo assembly in S. cerevisiae was used to build plasmids pY003 and pY005 containing the DNArep-PLsyn genome. pY003 was constructed from three DNA fragments: DNArep, PLsyn, and a fragment containing the yeast centromeric origin of replication CEN6/ARS4 and the auxotrophic marker URA3 for maintenance and selection in yeast. The PLsyn fragment was amplified from G363 with primers 41 ChDT and 58 ChDT, the DNArep fragment from G435 with primers 1289 ChD and 44 ChDT, and the CEN6/ARS4-URA3 fragment from pRS316 with primers 56 ChDT and 57 ChDT. For pY005, the CEN6/ARS4-URA3 fragment was replaced by a fragment that also included an E. coli origin of replication and an ampicillin resistance marker. The pUC-Amp-CEN6/ARS4-URA3 fragment was PCR-amplified from pRS316 using primers 57 ChDT and 64 ChDT that were designed to introduce 60-bp overlapping ends for efficient in vivo assembly. The PCR reaction mix contained (final concentrations) 1× Extreme Buffer (MERCK), 0.02 U µL–1 KOD DNA polymerase (MERCK), ∼0.2 ng µL–1 DNA template, 200 nM forward and reverse primers, 0.4 mM of each dNTP, and MilliQ water up to 50 µL final volume. The thermocycler protocol was set to 94 °C for 2 min for polymerase activation, followed by 35 cycles of 15 s at 98 °C, 20 s at 60 °C, and 60 s per kb at 68 °C depending on the length of the desired amplicon. PCR amplicons were analyzed by agarose gel electrophoresis and purified as described above. The DNA fragments were then pooled at a concentration of 100 fmol for the CEN6/ARS4-URA3 fragment and 200 fmol for the other two DNA fragments. Transformation in S. cerevisiae CEN.PK2-1C was carried out using the lithium acetate/single-stranded carrier DNA/polyethylene glycol method42. For selective growth, cells were plated on complete supplemental medium without uracil (CSM-URA) agar plates composed of 6.7 g L–1 yeast nitrogen base without amino acids (Formedium), 20 g L–1 glucose (Formedium), 0.77 g L–1 CSM-URA (MP Biomedicals), and 20 g L–1 agar (Euromedex). Plates were incubated at 28 °C for 2 days.

For pY003 and pY005 isolation from S. cerevisiae, 15 mL cultures were grown in liquid CSM-URA media at 28 °C with shaking at 200 rpm (Infors AG CH-4103 Bottmingen). Cells were harvested at mid-log phase by centrifugation at 3800 g for 5 min at 4 °C. Cell pellets were then washed with 20 mL of 100 mM Tris, pH 8.0, and centrifuged again at 3800 g for 5 min at 4 °C. The pellets were resuspended in 60 μL of a freshly prepared solution containing 100 mM Tris (pH 8.0), and 10 mM DTT. To this, 3 μL of RNase A (10 mg mL–1) were added, and the suspension was incubated at 30 °C for 10 min with shaking at 300 rpm (Eppendorf thermomixer comfort). After incubation, 8 μL of a solution containing 100 mM Tris (pH 8.0), 10 mM DTT, and 2 U μL–1 Zymolyase (20 T, from Arthrobacter luteus, Amsbio) were added to the cell suspension. The mixture was incubated again at 30 °C for 30 min with shaking at 300 rpm. Spheroplasting efficiency was evaluated by measuring the optical density at 660 nm (Cary 100 Scan UV-Visible Spectrophotometer) of the samples before and after Zymolyase treatment, with an 70–100% reduction in OD indicating successful spheroplast formation. Finally, plasmid DNA was extracted using the EZ-10 Spin Column Plasmid DNA Miniprep Kit, following the manufacturer’s instructions starting from the addition of Solution II (lysis buffer). DNA was eluted in 25 μL of prewarmed MilliQ water.

Plasmid pY005 was re-transformed in E. coli DH5α chemically competent cells. Transformed cells were recovered in 1 mL LB medium for 1 h and were then plated on 50 µg mL–1 ampicillin-LB agar plates. Following overnight incubation at 37 °C, a colony was picked to inoculate 5 mL 100 µg mL–1 ampicillin-LB medium. The liquid culture was grown at 37 °C and 280 rpm overnight. Plasmid was purified from 3 mL of liquid culture using the PureYield Plasmid MiniPrep System (Promega). DNA quality and concentration were determined via Nanodrop 2000c spectrophotometer (Isogen Life Science) followed by sequence verification by Oxford Nanopore sequencing (Plasmidsaurus). Three single-nucleotide substitutions were identified, two silent mutations in the p2 gene at positions E11 and K179, and mutation D28Y in the pssA gene. While no reports on inhibitory effects due to this mutation exist in literature, our results demonstrate that it does not abolish phosphatidylserine synthase activity.

Linear DNA fragments compatible with phi29-based DNA replication were obtained by nested PCR from pY003. The first PCR reaction (outer PCR) was performed using primers that annealed externally to the oriL and oriR flanks (50 ChDT and 51 ChDT). Subsequently, 1 µL of the product of the first PCR was used as the template for the second PCR reaction (inner PCR) using the 5’-phosphorylated primers 491E ChDT and 492E ChDT. The PCR reaction mix consisted of (final concentrations) 1× Extreme Buffer (MERCK), 0.02 U/µL KOD DNA polymerase (MERCK), 1 µL of template (Outer PCR: 1 µL of yeast-isolated plasmid; Inner PCR: 1 µL of the outer PCR), 300 nM forward and reverse primers (Outer PCR, 50 ChDT and 51 ChDT; Inner PCR, 491E ChDT and 492E ChDT), 0.4 mM of each dNTP, and MilliQ water up to 25 µL final volume. The thermocycler protocol was set to 94 °C for 2 min, followed by cycles of 10 s at 98 °C, 20 s at 60 °C, and 7.5 min at 68 °C (Outer PCR, 20 cycles; Inner PCR, 35 cycles).

For activity assays reported in Supplementary Fig. 23 a KOD polymerase variant with reduced mutagenesis rate, KOD One (TOYOBO), was used to generate linear templates from pY005. The PCR reaction mix consisted of 1× KOD One Master Mix, 300 nM of each forward and reverse primers (491 and 492 ChD), and ∼0.1 ng µL–1 of E. coli isolated plasmid DNA. The thermocycler protocol consisted of 35 cycles of 98 °C for 10 s and 68 °C for 50 s. All PCR amplicons were analyzed by agarose gel electrophoresis and purified as described earlier.

The lists of plasmids and primers are reported in Supplementary Tables 1 and 2, respectively.

Purification of SSB, DSB, and LactC2-mCherry
Փ29 SSB and DSB were expressed and purified as described in Ref. 43 and Ref. 17, respectively. Briefly, SSB was purified from B. subtilis cells infected with Փ29 bacteriophage. The cells were lysed, the proteins were extracted with buffer A (50 mM Tris-HCl (pH 7.5), 5% glycerol, 1 mM EDTA, 7 mM β-mercaptoethanol (BME)) supplemented with 0.2 M NaCl and precipitated from the supernatant with 65% ammonium sulfate. The pellet was dissolved in buffer A and the SSB-containing pellet was resuspended in a mixture of buffer A supplemented with 50% glycerol and 65% ammonium sulfate. The sample was centrifuged, the pellet discarded, and the supernantant diluted up to a conductivity of 4.2 KΩ−1 cm−1 and loaded onto a coupled phosphocellulose-DEAE-cellulose column. Following dialysis, the SSB stock concentration was 10 mg mL–1, stored in a buffer with 50 mM Tris, pH 7.5, 60 mM ammonium sulfate, 1 mM EDTA, 7 mM BME, and 50% glycerol.

DSB was heterologously expressed in E. coli BL21 (DE3) cells. The cell pellet was lysed and resuspended in buffer A supplemented with 0.8 M NaCl. After centrifugation and removal of DNA with polyethyleneimine, the supernatant was diluted with an equal volume of buffer A supplemented with 0.4 M NaCl and 0.04% polyethyleneimine. Following centrifugation, the DSB-containing pellet was resuspended with buffer A plus 1 M NaCl by stirring vigorously. The protein was precipitated with ammonium sulfate at 70% saturation, the pellet was resuspended in buffer A supplemented with 25% glycerol and loaded onto a phosphocellulose Whatman P11 column. The eluted protein sample was loaded on a Q Sepharose column and the eluted DSB was precipitated with ammonium sulfate at 70% saturation. After centrifugation, the pellet was resuspended to reach a DSB stock concentration of 10 mg mL–1, stored in a buffer with 50 mM Tris, pH 7.5, 0.1 M ammonium sulfate, 1 mM EDTA, 7 mM BME, and 50% glycerol.

LactC2-mCherry protein was expressed and produced as described in Ref. 19. Briefly, histidine-tagged LactC2-mCherry was expressed in E. coli BL21 (DE3) (NEB). The cell pellet was resuspended in lysis buffer, disrupted by sonication on ice, and centrifuged to remove the cell debris. The proteins were purified with HisPure Ni-NTA resin (Thermo Scientific). After elution, the fluorescent fractions were buffer-exchanged with storage buffer (50 mM HEPES-KOH, pH 7.5, 150 mM NaCl, 10% glycerol) using a 10-MWCO Amicon Ultra-15 centrifugal filter unit (Merck). The concentration was determined with a Bradford assay and the protein was stored in –80 °C. Before liposome staining, the protein stock vial was centrifuged at maximum speed (Eppendorf Centrifuge 5415R) for 10 min to spin down protein aggregates. If necessary, LactC2-mCherry protein stock was diluted in homemade PURE buffer (PB) consisting of 180 mM potassium glutamate monohydrate, 14 mM magnesium acetate tetrahydrate, and 20 mM HEPES at a pH of 7.6 (adjusted with potassium hydroxide) before usage.

In-liposome gene expression
Lipid-coated beads were prepared as explained in Refs. 2, 7 with minor modifications. A lipid mixture was prepared with chloroform-dissolved lipids (Avanti Polar Lipids) in a 5-mL round-bottom flask. The solution contained 49.5% DOPC, 33.7% DOPE, 12% DOPG, 3.8% 18:1 CL, and 1% DSPE-PEG-biotin mass composition, for a total mass of lipids of 2.02 mg. For confocal microscopy experiments the primary lipid composition was adjusted to include 0.05% mass of DOPE-Cy5. The resulting mix was supplemented with 25.4 μmol of rhamnose (Sigma-Aldrich) dissolved in methanol. 600 mg of 212–300 μm glass beads (acid-washed) were added to the 5 mL round-bottom flask containing the lipid/rhamnose solution, and the solvent was evaporated with a rotary evaporator (Heidolph) for 2 h at room temperature and 200 mbar. The lipid-coated beads were recovered and further dried in a desiccator overnight. The dried lipid-coated beads were flushed with argon and stored at −20 °C until use. In 1.5 mL Eppendorf tubes, PUREfrex 2.0 (GeneFrontier) reactions were assembled as recommended by the manufacturer using 500 pM DNA template (if not mentioned otherwise) and 0.75 U μL–1 of Superase·In RNase inhibitor (ThermoFisher). DNA assembled in vitro was used in all experiments, except for those described in Supplementary Figs. 4, 22–26, where the genome assembled in yeast was employed. When indicated, the IVTT mixture was supplemented with the required substrates and cofactors for DNA replication (300 µM dNTPs, 20 mM ammonium sulfate, 0.75 mg mL–1 SSB, and 0.21 mg mL–1 DSB, final concentrations) or/and PL synthesis (1 mM CTP, 500 µM G3P, 500 µM L-Serine, and 5 mM BME, final concentrations, the oleoyl-CoA precursor was added in a next step described below). 10 mg of lipid-coated beads freshly desiccated for 20-30 min were added to 20 µL of swelling solution. The sample in Eppendorf tube was kept in a 4 °C room for gentle rotation with an automatic tube rotator (VWR) for 30 min and was then subjected to four freeze-thaw cycles with alternating steps of dipping in liquid nitrogen and thawing on ice. Liposomes were recovered with a cut pipette tip and transferred to a PCR tube with DNase One (NEB) added to a final concentration of 0.1 U µL–1. For PLsyn activity, the sample was further transferred to another PCR tube with a pre-deposited O-CoA (Avanti Polar Lipids) film. The dried precursor film was prepared by dissolving the O-CoA powder in chloroform:methanol:water (40:10:1 vol. fractions), where after a solution containing 1 µg O-CoA was transferred to a PCR tube and the solvent was evaporated at ambient temperature and pressure. With the added liposome suspension, the final O-CoA concentration was 176 µM, and when needed (i.e., liposome suspension volumes changed), the pre-deposited O-CoA quantity was adjusted to maintain the same final concentration. Samples were incubated at 30 °C, 37 °C, or 34 °C for 16 h. A maximum of 15 µL liposome suspension was handled per PCR tube. If higher volumes were needed, the suspension was distributed across different PCR tubes with independently added O-CoA dried lipid film.

Flow cytometry
In a PCR tube, a liposome suspension (1.5 µL) was mixed in a 1:1 ratio with a 1000× diluted dsGreen (Lumiprobe) stock solution in PB and the sample was left to incubate in the dark at room temperature for 30 min. The 3 µL liposome-dsGreen mixture was further diluted by adding 197 µL of a 1:1000 dsGreen:PB solution. To remove possible remaining glass beads, the 200 µL solution was filtered through a 35 μm nylon mesh of a cell-strainer cap from 5 mL round-bottom polystyrene test tubes (Falcon). With a large volume pipette tip, 138.5 µL of the filtered solution was transferred to a 2 mL round-bottom tube to which 1.5 µL of 1:1000 dsGreen:PB solution and 10 µL of LactC2-mCherry probe were added to obtain a final LactC2-mCherry concentration of 300 nM and a final volume of 150 µL. The sample was incubated for 1 h before injection in a FACSCelesta flow cytometer (BD Biosciences) setup with a 488-nm laser and 530/30 filter for detection of dsGreen, and with a 561 nm laser and 610/20 filter for detection of LactC2-mCherry. Photon multiplier tube voltages were 375 V for forward scatter, 260 V for side scatter, 370 V for LactC2-mCherry, and 550 V for dsGreen detection. Loader settings were set to 50 µL injection volume with no mixing and 800 µL wash between sample runs. For each sample ∼20000 events were recorded. The raw data were analyzed and pre-processed using Cytobank (https://community.cytobank.org/) to filter out possible aggregates and liposome debris by selecting the main population in the SSC vs FSC plot, and subsequently filtering out low-fluorescence events when plotted against the SSC signal. The gating strategy with fluorescence intensity thresholding is described in Supplementary Fig. 5. Text files with all SSC-A, dsGreen, and LactC2-mCherry intensity values were exported from Cytobank and plotted with MATLAB.

Confocal microscopy
Liposome samples (2 µL) were transferred in custom-made glass chambers functionalized with 1 mg mL–1 BSA-biotin:BSA and 1 mg mL–1 Neutravidin, and pre-filled with 13 µL of a staining solution (1× dsGreen and 3 µM LactC2-mCherry in PB buffer). Chambers were incubated in the dark at room temperature for 1 h. In kinetic experiments (Supplementary Fig. 26), 7.5 µL of liposome suspension was mixed with 4.5 µL of dilution solution (1× PUREfrex 2.0 solution I, 2.6× dsGreen and 22.5 µM LactC-mCherry) and loaded on a dry oleoyl-CoA lipid film as described above. The sample was incubated in the dark for 30 min at 4 °C before being transferred into a microscopy chamber and further incubated in the dark for 4 °C to allow for liposome sinking. Confocal microscopy imaging was carried out on a Nikon Eclipse Ti (NIS-Elements AR software) using a 100× oil immersion objective. When needed, in situ incubation was performed with a heated stage at 34 °C. Laser settings for image acquisition were set to: 488-nm laser with 20 HV, -10 offset, and 1.0 intensity for dsGreen, 561-nm laser with 50 HV, -10 offset, and 1.00 intensity for LactC2-mCherry, and 640 nm laser with 95 HV, -5 offset, and 5.00 intensity for Cy5 membrane dye. Each sample was imaged by automated acquisition of 10 by 10 fields of view, stitched together with a 5% overlap. The sample height was adjusted manually to detect as many liposomes as possible, while also avoiding background debris.

Image analysis with SMELDit
Confocal images were analyzed manually with Fiji44 and automatically with SMELDit, an in-house-developed MATLAB script to automatically extract single liposome features while indexing each analyzed liposome. In short, the Cy5 and mCherry channels of each image are combined and convolved with a Laplacian filter kernel to determine membrane boundaries. To set what pixels belong to the inside of each liposome a binarization step, with a consistent cutoff based on previous data, followed by a filling and erosion step were utilized. The resulting binary image displayed separate segments, each representing the lumen of individual liposomes. This step was followed by an additional selection for filtering out the segments that could correspond to lipid aggregates or other noise sources. Then, all segments were analyzed individually for a circularity (C) check defined by C = P2/(4πA), where P is the perimeter length and A is the area of the segment. A ‘true liposome’ threshold was set for C values between 0.5 and 2.0. LactC2-mCherry aggregates were filtered out by rejecting events whenever LactC2-mCherry intensities were higher than a pre-set cutoff inside the lumen. The segments that passed these extra filtering steps were considered liposomes and were saved individually as 60 by 60-pixel cropped images with a given ID number. For each individual liposome SMELDit measures the apparent radius, average Cy5 intensity and variance on the membrane, average LactC2-mCherry intensity and variance on the membrane, and average dsGreen intensity and variance on the lumen. Per sample, SMELDit displays all single-liposome extracted data in a scatter/histogram interactive GUI on which the user can draw regions of interest (ROI) for extracting information about liposome subpopulations. Here, ROI 1–4 were defined in SMELDit using negative controls for thresholding. Finally, once ROIs are drawn, example liposomes from the ROI are retrieved as a liposome-montage. ROI liposome data can be saved separately, and ROI coordinates can also be saved and transferred to analyze another sample.

Deep learning-based image analysis
Two image-based machine learning algorithms for the identification of liposomes were trained based on the YOLOv7-tiny model (https://github.com/DanelonLab/YOLO-for-liposomes). The first algorithm was trained for the identification of liposomes using only the membrane channel (hereon referred to as yolov7-seg). The intention behind this model was to obtain an object detection tool for the identification of liposomes with reduced dependency on sample quality and stricter discrimination criteria that can later be implemented into diverse analysis pipelines, including SMELDiT. The total pool of data consisted of 26 JPEG images of 512 by 512 pixels, containing only the membrane channel from multiple samples of varied quality and contrast settings. A single class of object, Liposome, was used to label objects within images using Label Studio (https://github.com/HumanSignal/label-studio), by manually drawing a bounding box tightly around the membrane of desired liposomes (Supplementary Fig. 13). Labeled liposomes were objects that had a continuous, complete circumference (thus excluding edge-cropped, partially out of plane, and stacked vesicles), single membranes (excluding liposomes with multivesicular contents and multilamellarity), visually assessed high circularity, and, in cases of vesicle aggregates, vesicles where most of the individual membranes could be discerned. With these considerations, a total of 2689 objects were labeled. The dataset was then randomly split into two subsets: A training subset consisted of 21 images containing 2130 liposomes, and a validation subset with five images and 559 liposomes, accounting for 20.8% of the total dataset.

Model training was performed on a Google Collab pipeline available at https://github.com/DanelonLab/YOLO-for-liposomes, using an A100 GPU. To increase the robustness of the model, image modification parameters in the .cfg training file was activated. For the case of both expose and saturation, a modifier of 1.5 factors was instructed and rotation of images during training was set to 37 degrees. Once training of the model was concluded, the model was evaluated against the validation dataset, throughout a range of confidence thresholds going from 50 to 98%. Plotting of precision vs recall at each confidence threshold (Supplementary Fig. 13), it can be seen that further reduction in confidence threshold lowly impacts recall and precision below 85%, and precision remains above 70% even at low (50%) confidence. In addition, the model was tested using four independent images of 512 by 512 pixels in which 353 “desired” objects were identified manually. Yolov7-seg was able to identify 306 objects with 90% confidence, all the way to 357 objects at 75% confidence (Supplementary Fig. 13).

A second model for phenotype identification was trained using the pipeline described above with experimental data from synthetic cell module integration and negative controls. The dataset consisted of 42 JPEG images of 512 by 512 pixels that included all the channels from the original files. Six different classes of object were created to include the distinct phenotypes observed in fully active synthetic cells: From classes 1 to 6 in the order Replication, Replication condensate, Integration, Integration condensate, No phenotype, and PLsyn. In total, 4469 liposomes were labeled in the samples. The training subset contained 824 class 1, 74 class 2, 228 class 3, 24 class 4, 2224 class 5, and 628 class 6 objects. Training was performed using the same parameters as for yolov7-seg. The resulting model was tested against the validation dataset that included 467 liposomes. We confirmed the algorithm is capable of identifying all phenotypes it was trained on, but it goes through faster decrease of precision upon reduction of confidence, compared to YOLOv7-seg. In addition, the maximum recall achieved was 79% at a 50% confidence threshold (Supplementary Fig. 14). However, for categories with reduced representation in the training dataset, the model is prone to miss or fails to identify objects.

Quantitative PCR analysis
One to two microliters of liposome suspension were collected, incubated 15 min at 75 °C for DNAse I heat inactivation, and 100× diluted in MilliQ water. Ten microliter qPCR reactions were prepared with 1× PowerUP SYBR Green Master Mix (Applied Biosystems), 400 nM of each forward and reverse primer (976/977 ChD for p2, 980/981 ChD for p3, 1125/1126 ChD for pssA, 1119/1120 for plsB, 1410/1411 ChD for plsC, 1408/1409 ChD for cdsA), and 1 μL of the diluted liposome sample. Solutions were transferred to a qPCR 96-well plate (ThermoFisher) that was sealed with an adhesive transparent film (ThermoFisher) and spun down for 15 seconds. Measurements were performed on a Quantstudio 5 Real-Time PCR instrument (ThermoFisher) using the protocol: 2 min at 50 °C, 5 min at 94 °C, 45 cycles of 15 s at 94 °C, 15 s at 56 °C, 30 s at 68 °C, 5 min at 68 °C, and a final melting curve stage from 65 °C to 95 °C. Sample DNA concentrations were calculated from standard curves generated using DNA templates of known concentrations ranging from 1 fM to 1 nM (9 μL of qPCR reaction + 1 μL of DNA). Data were further analyzed with QuantStudio Design and Analysis software v1.4.3 (ThermoFisher).

Recovery of DNArep-PLsyn DNA from liposomes
Liposome samples diluted 100 times in MilliQ water for qPCR measurement were utilized also for DNA PCR recovery. Reactions were set up to either amplify three fragments or a single, near full-length, fragment from the DNArep-PLsyn genome. Twenty to 50 μL reaction solutions were assembled in a 1× Xtreme Buffer with 300 nM of each primer (all primers and details about the corresponding PCR amplification targets can be found on Supplementary Table 2), 0.4 mM of each dNTP, 2–5 μL of the diluted liposome solution, and 0.02 U µL–1 KOD DNA polymerase. The thermal cycler was programmed to follow 2 min at 94 °C for polymerase activation, and 30 cycles of (98 °C for 10 s, 60 °C for 20 s, 68 °C for 2 min for fragments A, B, C, and 5 min for one-fragment PCR-recovery). PCR products were analyzed by agarose gel electrophoresis.

Bulk IVTT reactions
Ten microliter bulk IVTT reactions were performed with PUREfrex 2.0 using 500 pM DNA template according to the manufacturer’s guidelines (unless specified otherwise). To visualize synthesized protein products, the reaction was supplemented with 1 μL GreenLys solution (FluoroTect GreenLys, Promega) and incubated for 16 h at 37 °C. Samples were then supplemented with 1 μL of RNase A (4 mg mL–1) and 1 μL of RNase One (10 U μL–1), and incubated for 1-2 h at 37 °C for complete RNA digestion. 10 μL of the RNA-digested sample were mixed with 1× Laemmli sample buffer and 10 mM of DTT, final concentrations. The reaction mixtures were incubated at 95 °C for 5 min and loaded on a 12% SDS-PAGE gel that was run for 1 h at 100 V, followed by another 50–60 min at 130-160 V. GreenLys labeled proteins were visualized on a fluorescence gel imager (Typhoon, Amersham Biosciences) using Cy2 (488 nm), Cy3 (532 nm), and Cy5 (635 nm) lasers with band-pass filters of 515-535 nm for Cy2, 560-580 nm for Cy3, and 655-685 for Cy5. Laser PMT voltages were set to 500 for Cy2 and automatic adjustment for Cy3 and Cy5. The SDS-PAGE gel was further stained overnight with InstantBlue (expedeon), destained the next day with MilliQ water, and visualized with a ChemiDoc imaging system (Bio-Rad).

Analysis of lipid content by LC-MS
Two to four microliters of liposome suspension were diluted 10× in a sample preparation solution consisting of a 1:1:98 ratio of 0.5 M EDTA:200 mM acetylacetone:100% methanol. The solution was sonicated for 10 min, and centrifuged at 16.1 g and room temperature for 5–10 min. 15 μL of the supernatant with the soluble lipid phase were transferred to a 25 μL glass insert within a 2 mL LC-MS glass vial, further diluted 4× with the sample preparation solution, and stored under argon at −20 °C until use (less than a week). For LC-MS sample analysis, ∼10 μL sample kept at 4 °C were injected into a 6460 Triple Quad LC-MS stocked with a ACQUITY UPLC Peptide CSH C18 Column with a mobile phase A of 0.05% ammonium hydroxide and 2 mM acetylacetone in water, and a mobile phase B of 80% 2-propanol, 20% acetonitrile, 0.05% ammonium hydroxide and 2 mM acetylacetone, at a flow rate of 300 μL min–1 and column temperature of 60 °C. An A:B ratio of 70:30 was set to equilibrate the column. Upon sample injection, the A:B ratio was slowly changed to 100% mobile phase B and kept for 2 min. Then, the 70:30 ratio was gradually restored and kept until the end of each sample run. Transitions were established by multiple reaction monitoring2,45. The lipids were ionized, selected by mass in a first ion filter, fragmented by collisions with nitrogen gas in a collision cell and then the fragment masses were monitored in another ion filter. Data were analyzed with Skyline-daily. Peak areas were exported and normalized to DOPG peak areas. When two injections were done per sample, the averaged peak area was considered.

Statistics and reproducibility
For data presenting graphs, the figure legends provide the number of biological replicates that were performed. All experiments have been reproduced at least three times (independent, biological repeats) except for some data shown in Supplementary Fig. 11, 16, 18, 19, 22, for which two repeats were performed. For the gel presented in Fig. 1e, data from two additional independent experiments are shown in Supplementary Fig. 3 (uncropped gels). Statistical tests were performed in Figs. 2e and 5f as described in the legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by the Danelon group presents the construction of synthetic cells integrating in vitro DNA replication and membrane biosynthesis. The authors designed a synthetic genome encoding both DNA replication proteins and membrane biosynthesis enzymes, which are expressed using the PURE system. The experimental design is thoughtful, and the data are clearly presented.\nMain concerns:\nWhile the system is functional in principle, there are two major limitations—both acknowledged by the authors (lines 281–283):\n1. The majority of liposomes did not exhibit the joint phenotype of both DNA replication and phospholipid biosynthesis, indicating low co-expression efficiency. The proportion of functional synthetic cells remains limited, raising concerns about the robustness of the system.\n2. Despite lipid synthesis, no detectable membrane expansion was observed. This contrasts with previous work (e.g., Exterkate et al., ACS Synth. Biol. 2018, 7, 1, 153–165), where purified enzymes enabled membrane growth. This suggests that the current system does not produce sufficient phospholipids to support physical expansion, challenging the claim of “membrane biosynthesis” in the title.\nTo address these concerns, the authors should better improve the system’s efficiency to overcome these limitations.\nSpecific comments:\n1. Figure 1a: All abbreviations should be defined in the figure legend for clarity.\n2. Line 115: The proportion of liposomes showing both DNA replication and phospholipid synthesis (ROI 2) is reported as less than 3%, which is particularly low. The authors should discuss strategies to increase this percentage, which is critical for realizing a functional synthetic cell.\n3. Figure 3a: It is unclear whether the small panels on the right are cropped from the large panel on the left. The correspondence between the images should be clarified. Also, the border colors seem inconsistent with the rest of the manuscript. For consistency, DNA replication should be indicated in magenta and phospholipid synthesis in blue.\n4. Lines 171–172: The authors note that the yield of newly synthesized lipids is insufficient for visible membrane growth. Could they estimate the minimum amount of lipids required for such growth? Adding chemically synthesized lipids might help determine this threshold and allow comparison with the lipid yield from the current system.\n5. Line 180: While the DNA replication and phospholipid synthesis modules do not interfere with each other, how can their activities be maximized? Enhancing both pathways may help increase the ROI 2 population and improve system efficiency.\n6. The resolution of all supplementary figures is too low. For example, plasmid maps are not legible. Please provide high-resolution versions in the supplementary information.\n7. Figure S1b legend: The phrase “without (left) and without (right)” appears to be an error. Please revise for clarity.", "reviewer_2": "This manuscript presents a synthetic DNA genome that drives both self-replication and membrane phospholipid biosynthesis within liposomes. The authors express six genes from a linear construct—two encoding the DNA replication machinery and four involved in phospholipid synthesis—using the PURE system. The genome is capable of self-replication, and the encoded enzymes catalyze the production of DOPS (1,2-dioleoyl-sn-glycero-3-phospho-L-serine), a key phospholipid intermediate. These processes are reconstituted in liposomes, establishing a platform for studying the integration of core cellular functions in a minimal synthetic context.\nThe combination of confocal microscopy and computational analysis is a key strength of the study. The authors quantify single-vesicle activity using their custom software SMELDit (https://github.com/DanelonLab/SMELDit), which extracts fluorescence intensity measurements for DNA replication (dsGreen) and membrane biosynthesis (LactC2-mCherry).\nHowever, the current classification strategy—based on fixed intensity thresholds and a quadrant-based map dividing vesicles into four phenotypes—is limited. It imposes artificial boundaries that may not reflect the true distribution of phenotypic states and is highly sensitive to variability in fluorescence intensity across experiments.\nA more robust and unsupervised strategy – such as dimensionality reduction using UMAP (Uniform Manifold Approximation and Projection) followed by density-based clustering (e.g., HDBSCAN) would enable phenotype identification based on the structure of the data itself, without imposing predefined categories. This would improve reproducibility and better account for biological variability and differences across experimental replicates, avoiding arbitrary thresholds.\nMoreover, the imaging dataset contains rich spatial and morphological information that remains underexploited. The current analysis appears to rely primarily on average fluorescence intensities, but additional features such as vesicle area, circularity, membrane texture, internal intensity variance, and the presence of localized replication foci could provide a more complete description of vesicle states. These features could then be integrated into the clustering pipeline, enhancing the ability to resolve subtle or intermediate phenotypes and to explore relationships between genotype, phenotype, and compartmental architecture.\nIn addition to rethinking the classification approach, image segmentation itself could benefit from modern deep learning-based methods, which have shown superior performance in detecting and segmenting biological compartments, particularly in heterogeneous and noisy microscopy data. Neural network architectures trained on a representative annotated dataset would likely improve segmentation accuracy, reduce bias from manual parameter tuning, and allow for better generalization across experiments.\nIn terms of implementation, while the availability of the SMELDit code is appreciated, the fact that it relies on MATLAB—an expensive, engineer-oriented commercial platform—limits accessibility and reuse, especially among researchers in the life sciences. Reimplementing the pipeline using an open-source, community-supported language such as Python (e.g., with napari, or PyTorch) or Java (e.g., Fiji/ImageJ) would significantly increase its utility and adoption.\nFinally, since the GitHub repository does not include example datasets or segmentation outputs due to file size constraints, I encourage the authors to provide at least one representative dataset—including raw images, segmentation masks, and extracted features—to support reproducibility and allow others to test and extend the tool.\nIn summary, this is a valuable and well-executed study that advances synthetic cell engineering by integrating genome-driven replication and membrane biosynthesis within liposomes. The imaging data is central to the conclusions of the manuscript. Incorporating more advanced computational methods for segmentation, feature extraction, and phenotype classification would enhance the reproducibility and interpretability of the analysis, ultimately increasing the impact of the work.", "reviewer_3": "Sierra et al. describe the integration of two fundamental modules likely required to achieve the development of a synthetic cell, namely DNA replication and lipid synthesis (metabolism). They demonstrate that by combining PURE with a linear template encoding 6 proteins (2 required for DNA replication, and 4 enzymes required for lipid synthesis) and encapsulating the reaction in liposomes. Using FACS and direct imaging of these liposomes, they were able to show that both DNA replication and lipid synthesis occur in these liposomes, although the co-occurrence of these two processes was a surprisingly rare event compared to each module alone. The FACS data has some shortcomings in regards to demonstrating that DNA replication occurred, although these shortcomings are for the most part resolved with the direct imaging approach. Although it might be worthwhile to consider conducting a time-course experiment if possible. Overall, the manuscript is well written, and for the most part well supported by data (with the exception of Fig 2 and corresponding Supp figures). The authors should address these issues in a revised document. Overall, this manuscript demonstrates a significant step forward which will be of general interest to the field.\nSpecific comments:\nThere appears to be a copy – paste mistake in Figure S5 (page 6) as the two figures in the last two rows – middle column are identical.\nAlthough PS synthesis seems to be fairly robust and relatively clearly visible based on the FACS data provided in Figure S5, whether DNA replication was functional is much less clear / apparent. For example, the seemingly most obvious DNA replication event occurred in replicate 2 at 34C, but the confounding factor is the fact that the positive control data points are in general about one order of magnitude higher than the negative control. Could this be due to simply having generally higher DNA concentrations from the beginning in the positive control liposomes, or is the expectation that all liposomes are expected to perform DNA replication. The analysis on the other hand (using a threshold) suggests that the expectation is that only a fraction of liposomes will be able to perform functional DNA replication. Furthermore, in replicate 6 at 34C it appears that the threshold for the NC and PC is not the same! In general, the evidence for DNA replication based on Figure S5 seems relatively weak. Would it be possible to perform a FACS analysis of the same samples both at T=0 and at 16 hours, by subjecting a fraction of the reaction to FACS at the beginning of the reaction. This would provide a good baseline for identifying whether the liposome populations shift in the FACS measurement relative to T0.\nThe data shown in Fig 2e should be supported by a statistical test to indicate whether or not there are indeed significant differences in DNA replication observed. Since this qPCR is a bulk assay it likely can’t exclude the possibility that DNA replication, if it occurs, actually occurs inside liposomes or outside of liposomes (liposomes could burst and release their content and DNA replication could occur in the supernatant)?\nDirect imaging of liposomes as shown in Fig 3 is more convincing than the FACS data in regards to DNA replication, although here it would be good to provide a time-course as well (image liposomes at T=0 and then repeat imagine at reasonable intervals of every few hours). The following experiments in Fig 4 and 5 also nicely add to the evidence that DNA replication indeed occurs in liposomes. Could it therefore be that FACS is maybe not an ideal method for characterizing these liposomes as they could rupture and disintegrate in the FACS for example?\nIt remains somewhat curious why such a low fraction of liposomes are exhibiting DNA replication as well as liposome synthesis…"}, "author_response": "Rebuttal letter for:\nManuscript: \"A synthetic cell with integrated DNA replication and membrane biosynthesis\"\nby Restrepo Sierra et al.\nArticle reference: NCOMMS-25-21548\nWe thank the reviewers for recognizing the importance of our work and for their constructive\ncomments that helped us improve the manuscript. The Referee reports are in black text and our\npoint-by-point responses are in blue text. Changes in the manuscript (main text and Supplementary\ninformation) are highlighted in red text.\nWe are pleased to provide you with a substantially revised manuscript that addresses all referees’\nconcerns. In particular:\na) We performed additional experiments with a more robust source of DNA and managed to\nsignificantly improve the efficiency of the system.\nb) We fully engaged with the guidance from reviewer #2 and made our custom software SMELDit\nbroadly accessible on Python. The code and related files are available on GitHub.\nc) We used a neural network-based image analysis pipeline for segmentation and phenotype\nclassification and made the code and related files available on GitHub.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript by the Danelon group presents the construction of synthetic cells integrating\nin vitro DNA replication and membrane biosynthesis. The authors designed a synthetic genome\nencoding both DNA replication proteins and membrane biosynthesis enzymes, which are\nexpressed using the PURE system. The experimental design is thoughtful, and the data are\nclearly presented.\nReply: We thank the reviewer for these positive comments.\nMain concerns:\nWhile the system is functional in principle, there are two major limitations—both\nacknowledged by the authors (lines 281–283):\n1. The majority of liposomes did not exhibit the joint phenotype of both DNA replication and\nphospholipid biosynthesis, indicating low co-expression efficiency. The proportion of\nfunctional synthetic cells remains limited, raising concerns about the robustness of the system.\nclearly shows room for improvement, more than hundred vesicles with both module activities\nare routinely obtained in a sample of 2 µL and in a single imaging z-plane (see Movie). This\nwould mean that we likely produce thousands of active vesicles with the joint phenotype in a\nfull reaction of 10-20 µL. Moreover, we cannot exclude that liposomes assigned as ‘inactive’\nbased on fluorescence intensity thresholding would present some basal activity that is below\nthe detection sensitivity.\nNonetheless, we agree that the fraction (not per se the absolute number) of functional synthetic\ncells is rather low, suggesting that some factors limit the system’s efficiency.\nIn the revised manuscript, we discussed the current limitations of the system and\nexperimentally tested a hypothesis, which significantly improved robustness (see below).\nWe extended the first paragraph of the Discussion as follows:\n“Although we routinely obtained more than a hundred vesicles exhibiting the joint phenotype\nper sample, these only represent about 8% of the total liposome population when using a DNA\ntemplate generated by overlapping PCR. We suspected that the robustness of the system may\nbe limited by the DNA quality and purity. Using a stable source of DNA, namely a sequence-\nverified circular plasmid to generate the linear DNArep-PLsyn template, improved the system’s\nefficiency over a factor of 3.5 with ~30% of fully functional synthetic cells. We attribute this\nimprovement to a higher purity of the full-length DNA that also enables to work with increased\nconcentrations of active template. The remaining fraction of nonfunctional synthetic cells\nprobably arises from deficient molecular compositions inside vesicles, which cannot support all\nfunctions concurrently. For instance, differences in the loading of DNA or translation\nmachinery between liposomes probably lead to varying levels of expressed genes, while\nuneven supply of substrates or cofactors for DNArep and PLsyn would result in varying outputs\nof the modules. Even if it is clear that prototype synthetic cells will not have the level of control\nand robustness of natural living cells, a major challenge will be to mitigate this heterogeneity\nand increase the proportion of active vesicles.”\n2. Despite lipid synthesis, no detectable membrane expansion was observed. This contrasts\nwith previous work (e.g., Exterkate et al., ACS Synth. Biol. 2018, 7, 1, 153–165), where purified\nenzymes enabled membrane growth. This suggests that the current system does not produce\nsufficient phospholipids to support physical expansion, challenging the claim of “membrane\nbiosynthesis” in the title.\nmembrane. In the Discussion we wrote: “challenges remain to achieve physical growth of\nliposomes upon lipid biosynthesis”.\nIn the study of Exterkate et al. (new ref. 25), the authors have reconstituted detergent-\nsolubilized enzymes into small/large vesicles, showing nearly stochiometric conversion of fatty\nacid substrates into phospholipids. Reactions were not compartmentalized in liposomes and\nthe enzymes were not genetically encoded. De novo membrane synthesis and vesicle growth\nwere also demonstrated by the group of Devaraj (new refs. 26, 27). Here too, the experimental\nframework radically differs from ours, and the respective advantages and shortcomings are\ndifferent.\nWe agree that these previous studies should be cited as they show that alternative methods for\nlipid production exist and may be coupled to gene expression with PURE system and DNA\nreplication. Actually, we attempted to produce phospholipid analogues by native chemical\nligation, as developed in ref. 26. The precursors sodium 2-mercaptoethane-sulfonate (MESNA)\noleate, 1-oleoyl-2-(L-Cys)-sn-glycero-3-phosphocholine (o-LPC), and 1-palmitoyl-2-(L-Cys)-sn-\nglycero-3-phosphocholine (p-LPC) were kindly provided by the Devaraj lab. Unfortunately, the\nreactive precursors severely inhibited gene expression in PURE system and we decided to not\npursue this strategy for now. We also tried to utilize oleic acid as a phospholipid precursor by\nextending the Kennedy pathway upstream with the FadD enzyme, but phospholipid synthesis\nyield (here DOPE) was not significantly higher than with oleoyl-CoA despite a 10-fold higher\nstarting concentration (PhD thesis Duco Blanken, link:\nhttps://repository.tudelft.nl/record/uuid:363f4643-0a68-4726-9945-e8daf6e0350c)\nAccordingly, we added in the Discussion: “While the DNA amplification fold may be sufficient\nfor the partitioning of the genetic material amongst daughter vesicles upon division, the\nbottleneck for a cell cycle is on the yield of synthesized phospholipids. With the produced DOPS\nrepresenting only <7% of the total lipid amount, the current system cannot support physical\nexpansion of the membrane. Using purified, detergent-solubilized enzymes reconstituted into\nlarge vesicles, Exterkate et al. showed efficient conversion of the fatty acid precursor, which\nenabled membrane growth [25]. In other studies, de novo membrane synthesis was\ndemonstrated with phospholipid analogues produced by native chemical ligation [26,27]. The\ncompatibility of these approaches with PURE system driving the expression of an internal DNA\nprogram remains to be investigated.”\nIn our opinion, the fact that phospholipids are synthesized and stably insert in the membrane\ndoes not conflict with ‘membrane biosynthesis’ in the title. We acknowledge however that the\nterm might be confounding and we changed ‘membrane’ to ‘lipid’ in the new title. The fact that\nphysical expansion was not detected shows limitations that are more extensively discussed in\nthe revised manuscript.\novercome these limitations.\nReply: We engaged with the suggestion of the reviewer and performed additional experiments\nto overcome some of the limitations. We managed to improve the system’s efficiency by\nconstructing a circular plasmid as a stable source of DNA to generate the linear DNArep-PLsyn\ngenome instead of overlapping PCR. Expression of 1 nM of this new template in liposomes\nresulted in 30% of liposomes exhibiting the joint phenotype, which represents an improvement\nof 3.5 times.\nIn the Results section, line 331, we added a paragraph and included four new supplementary\nfigures reporting these findings:\n“…DNA libraries. High amounts of the PCR-amplified genome could be obtained, allowing for\nnanomolar input concentrations. Its expression in liposomes improved the robustness and\nefficiency of the system, yielding ~30% (vs 8%) of fully active synthetic cells and over 37% (vs\n17%) with at least one functional module, while reducing variability between replicates\n(Supplementary Figs. 23-25). The change of phenotype map compared to Fig. 3c (template\nobtained by overlapping PCR) suggests an increased ability of DNArep-active liposomes to also\nproduce phospholipids. Time-lapse imaging of phenotype development shows that DNArep\nprecedes PLsyn and the activity of both modules reaches a plateau after 6 hours\n(Supplementary Fig. 26).”\nSpecific comments:\n1. Figure 1a: All abbreviations should be defined in the figure legend for clarity.\nReply: We defined all abbreviations in the revised manuscript.\n2. Line 115: The proportion of liposomes showing both DNA replication and phospholipid\nsynthesis (ROI 2) is reported as less than 3%, which is particularly low. The authors should\ndiscuss strategies to increase this percentage, which is critical for realizing a functional\nsynthetic cell.\nReply: The exact percentage of liposomes detected in ROI 2 varies from one biological repeat to\nanother, but is generally lower than 10%. In the revised manuscript, we extensively discussed\nthe current limitations and reported new results showing an improved efficiency with ~30%\nliposomes in ROI 2. See our replies to the comments above.\n3. Figure 3a: It is unclear whether the small panels on the right are cropped from the large\npanel on the left. The correspondence between the images should be clarified. Also, the border\n4\ncolors seem inconsistent with the rest of the manuscript. For consistency, DNA replication\nshould be indicated in magenta and phospholipid synthesis in blue.\nReply: We accidently swapped the frame colors of ‘DNA replication’ and ‘PS synthesis’ in the\nlarge panel. Thank you for pointing this out. For clarity, we added in the figure legend: “The\ncropped images correspond to the framed liposomes in the large panel.”\n4. Lines 171–172: The authors note that the yield of newly synthesized lipids is insufficient for\nvisible membrane growth. Could they estimate the minimum amount of lipids required for such\ngrowth? Adding chemically synthesized lipids might help determine this threshold and allow\ncomparison with the lipid yield from the current system.\nReply: We estimate that an increase of the liposome diameter of 20% would be required to\nunambiguously observe membrane growth. The microscope resolution (around 250 nm)\ntheoretically enables smaller changes to be detected but tracking of single vesicles over\nmultiple time frames may result in z-plane changes, which eventually can alter length\nmeasurements. Moreover, FRET-based assays could be used to monitor smaller changes but\nthe present considerations pertain only to direct visualization of liposome growth.\nFor a 5-µm diameter liposome undergoing a 20% increase in size ( = 5 → 6 µm), the\ncorresponding excess membrane surface area is 34.6 µm2 (A = 113.1 – 78.5 µm2, i.e., 44%\nincrease). Assuming a lipid molecular surface area of 70 Å2 (corresponding to DOPC, the most\nabundant lipid species in our vesicles), the additional lipid amount required is 98.7×106 lipids\nper vesicle.\nAt the sample level, in the ideal case scenario, where all oleoyl-CoA (176 µM) is consumed and\nfully converted into DOPS, 88 µM of phospholipids are synthesized (DOPS is a two-acyl chain\nlipid). Given a total concentration of lipids of 2 mM when starting genome expression, and under\nthe assumptions i-iii reported below, one can calculate that the liposome surface area increases\nby 4.4% (0.088 mM DOPS is 4.4% of 2 mM total lipids and the membrane surface area scales\nlinearly with the number of lipids).\nAssumptions:\n(i) lipid synthesis is homogenously distributed across all liposomes\n(ii) all synthesized lipids assemble into the membrane\n(iii) all lipid species in the membrane have the same molecular surface area.\nAs the diameter scales with the square root of the area, the liposome size increases by 2.1%. For\na liposome diameter of 5 µm, this means a diameter increase of 105 nm, which is below the\ndetection limit of our light microscope and much lower than a vesicle growth of 1 µm for\nunambiguous observation.\nThe concentration of O-CoA that needs to be converted into DOPS to support an increase of 1\nµm in diameter (i.e., a surface increase of 44%) would be 1.76 mM (2 × 880 µM DOPS), which\nlargely exceeds the solubility of O-CoA in PURE system (estimated to be <200 µM given its\nprecipitation with magnesium ions) and may cause inhibition to lipid synthesis\n5\n(https://pubmed.ncbi.nlm.nih.gov/7018587/). Continuous addition of smaller concentrations of\nacyl-CoA may be possible but will require to design a dedicated flow-cell imaging chamber.\nFinally, we note that to sustain an increase of the liposome surface of 44%, DOPS will likely\nhave to be converted into PE by introducing a downstream enzyme of the Kennedy pathway\n(Psd) to balance the surface charge density.\nAdding chemically synthesized lipids (see our reply to Comment #2) or experiments of\nSUV/LUV fusion may indeed help determine the threshold of extra lipids that is required to\nobserve growth in our conditions.\n5. Line 180: While the DNA replication and phospholipid synthesis modules do not interfere\nwith each other, how can their activities be maximized? Enhancing both pathways may help\nincrease the ROI 2 population and improve system efficiency.\nReply: The new results presented in Supplementary Figs. 23-26 demonstrate that the system’s\nefficiency can be improved with a higher concentration of a DNA genome of higher\nquality/purity. This was achieved by using a yeast-assembled plasmid as a PCR template to\ngenerate the linear genome instead of the one produced by overlapping PCR. Strategies for\nfurther improvement of module integration are discussed in the revised manuscript. Please see\nalso our reply to Comment #2.\nIn the original Discussion we proposed to optimize the metabolic balance and resource\nallocation for gene expression, to introduce temporal control over gene expression, and to\nevolve the gene-encoded proteins.\n6. The resolution of all supplementary figures is too low. For example, plasmid maps are not\nlegible. Please provide high-resolution versions in the supplementary information.\nReply: Resolution is good enough on our PDF file. We think resolution may be reduced when\nuploading the file on the submission portal. We uploaded a new Supplementary Information\nfile with (hopefully) higher resolution. Moreover, we now provide a link to access plasmid\nmaps on Github, link: https://github.com/DanelonLab/pMAR3/tree/DNA-sequences\n7. Figure S1b legend: The phrase “without (left) and without (right)” appears to be an error.\nPlease revise for clarity.\nReply: We corrected as “without (left) and with (right)”\nThis manuscript presents a synthetic DNA genome that drives both self-replication and\nmembrane phospholipid biosynthesis within liposomes. The authors express six genes from a\nlinear construct—two encoding the DNA replication machinery and four involved in\nphospholipid synthesis—using the PURE system. The genome is capable of self-replication, and\nthe encoded enzymes catalyze the production of DOPS (1,2-dioleoyl-sn-glycero-3-phospho-L-\nserine), a key phospholipid intermediate. These processes are reconstituted in liposomes,\nestablishing a platform for studying the integration of core cellular functions in a minimal\nsynthetic context.\nThe combination of confocal microscopy and computational analysis is a key strength of the\nstudy. The authors quantify single-vesicle activity using their custom software SMELDit\n(https://github.com/DanelonLab/SMELDit), which extracts fluorescence intensity\nmeasurements for DNA replication (dsGreen) and membrane biosynthesis (LactC2-mCherry).\nHowever, the current classification strategy—based on fixed intensity thresholds and a\nquadrant-based map dividing vesicles into four phenotypes—is limited. It imposes artificial\nboundaries that may not reflect the true distribution of phenotypic states and is highly\nsensitive to variability in fluorescence intensity across experiments.\nReply: We generally agree that classifying phenotypes merely using fluorescence intensity\nthresholds is somehow limiting. Yet, it is a good method to assess the activity of the two\nmodules in a quantitative way. As described below, we also used a machine learning-based\nmethod to segment images and identify liposomes with features that go beyond intensity\nthresholds. The code is available on Github: https://github.com/DanelonLab/YOLO-for-\nliposomes\nNote that for each independent experimental condition a corresponding negative control was\nperformed for each module. In each independent repeat, these negative controls were used to\ngenerate the regions of interest for each phenotype. We observed that throughout the different\nrepeats, the thresholds could successfully be extrapolated. Then, when merging all experiments\n(as shown for instance in Fig 3b), we could gate the ROls as effectively as when done\nindividually.\nAlthough the classification of phenotypes relies on average fluorescence intensity values,\nSMELDit can easily be upgraded to also extract other physical parameters. We modified the\ncode to store and retrieve the perimeter, circularity, and variance of the Cy5 signal (to further\nfacilitate the cleaning of data) for each individual liposome detected.\nA more robust and unsupervised strategy – such as dimensionality reduction using UMAP\n(Uniform Manifold Approximation and Projection) followed by density-based clustering (e.g.,\nwithout imposing predefined categories. This would improve reproducibility and better\naccount for biological variability and differences across experimental replicates, avoiding\narbitrary thresholds.\nReply: Thresholding is not arbitrary. As indicated above, thresholds are defined based on\nfluorescence intensity of negative controls. To note, SMELDit is not restricted to phenotype\nidentification; it also enables quantitative analysis by extracting physical parameters, such as\nfluorescence intensity, variance, and circularity.\nIn the revised manuscript, we used a neural network-based image analysis pipeline to classify\nliposomes based on multimodal features that go beyond fluorescence intensity thresholding.\nThe approach remains ‘supervised’ as we train the model with two/three sets of liposome\nimages predefined by the user as ‘joint phenotype’, DNArep, or PLsyn.\nTo inform the reader about the existence of unsupervised strategy, we added in the Discussion\nthe following sentence and new reference 42: “Finally, incorporating more advanced\ncomputational methods, like unsupervised learning [42], for segmentation, feature extraction,\nand phenotype classification would enhance reproducibility of the analysis, while expanding\nthe discovery of novel properties.”\nMoreover, the imaging dataset contains rich spatial and morphological information that\nremains underexploited. The current analysis appears to rely primarily on average fluorescence\nintensities, but additional features such as vesicle area, circularity, membrane texture, internal\nintensity variance, and the presence of localized replication foci could provide a more\ncomplete description of vesicle states. These features could then be integrated into the\nclustering pipeline, enhancing the ability to resolve subtle or intermediate phenotypes and to\nexplore relationships between genotype, phenotype, and compartmental architecture.\nReply: Although liposome classification with SMELDit relied on average fluorescence intensity\nvalues, additional features, such as vesicle area and radius, circularity, intensity variances in all\nthree channels could also be extracted to provide a more complete description of the liposome\nstates. For instance, the lumen intensity variance of dsGreen signal could be used to identify\nvesicles with localized replication foci.\nWe added some information in the Customization section of the pySMELDit Readme file.\nFor implementing additional features or improving liposome recognition, one can simply\nswitch out the image_analysis function inside image_analysis_module.py for whatever new\nalgorithm. Just make sure it saves the same metrics to its data file. Additional metrics to be\ndisplayed in the SMELDit GUI can be manually added to the self.metric_names dict in\nSMELDit.py.\nimage segmentation and phenotype classification.\nIn addition to rethinking the classification approach, image segmentation itself could benefit\nfrom modern deep learning-based methods, which have shown superior performance in\ndetecting and segmenting biological compartments, particularly in heterogeneous and noisy\nmicroscopy data. Neural network architectures trained on a representative annotated dataset\nwould likely improve segmentation accuracy, reduce bias from manual parameter tuning, and\nallow for better generalization across experiments.\nReply: We have trained a YOLOv7-tiny algorithm for the detection of vesicles and phenotype\nidentification. This machine learning algorithm recognizes liposomes by their membrane\nchannel and generates an output file including the object classification, a detection confidence\nand the coordinates of a bounding box spatially delimiting the object. We made the code\navailable on GitHub at https://github.com/DanelonLab/YOLO-for-liposomes, and included a\nREADME file and two subfolders with example images.\nWe added in Methods a new section “Deep learning-based image analysis”:\n“Two image-based machine learning algorithms for the identification of liposomes were\ntrained based on the YOLOv7-tiny model (https://github.com/DanelonLab/YOLO-for-\nliposomes). The first algorithm was trained for the identification of liposomes using only the\nmembrane channel (hereon referred to as yolov7-seg). The intention behind this model was to\nobtain an object detection tool for the identification of liposomes with reduced dependency on\nsample quality and stricter discrimination criteria that can later be implemented into diverse\nanalysis pipelines, including SMELDiT. The total pool of data consisted of 26 JPEG images of\n512 by 512 pixels, containing only the membrane channel from multiple samples of varied\nquality and contrast settings. A single class of object, Liposome, was used to label objects within\nimages using Label Studio (https://github.com/HumanSignal/label-studio), by manually\ndrawing a bounding box tightly around the membrane of desired liposomes (Supplementary\nFig. 13). Labelled liposomes were objects that had a continuous, complete circumference (thus\nexcluding edge-cropped, partially out of plane, and stacked vesicles), single membranes\n(excluding liposomes with multi-vesicular contents and multilamellarity), visually-assessed\nhigh circularity, and, in cases of vesicle aggregates, vesicles where most of the individual\nmembranes could be discerned. With these considerations, a total of 2,689 objects were\nlabelled. The dataset was then randomly split in two subsets: A training subset consisted of 21\nimages containing 2,130 liposomes, and a validation subset with 5 images and 559 liposomes,\naccounting for 20.8% of the total dataset.\nModel training was performed on a Google Collab pipeline available at\nhttps://github.com/DanelonLab/YOLO-for-liposomes, using an A100 GPU. To increase the\nrobustness of the model, image modification parameters in the .cfg training file was activated.\nFor the case of both expose and saturation, a modifier of 1.5 factors was instructed and rotation\nof images during training was set to 37 degrees. Once training of the model was concluded, the\nmodel was evaluated against the validation dataset, throughout a range of confidence\nthresholds going from 50% until 98%. Plotting of precision vs recall at each confidence\nthreshold (Supplementary Fig. 13), it can be seen that further reduction in confidence\nthreshold lowly impacts recall and precision below 85%, and precision remains above 70%\neven at low (50%) confidence. In addition, the model was tested using 4 independent 512 by\n512 pixels in which 353 “desired” objects were identified manually. Yolov7-seg was able to\nidentify 306 objects with 90% confidence, all the way to 357 objects at 75% confidence\n(Supplementary Fig. 13).”\nFig. S13. Deep learning-assisted liposome identification. YOLO detection output with 80%\nconfidence (b) vs manual selection of desired vesicles (a). Overall, most liposomes were\ncorrectly identified, although a tendency for the model to label cropped liposomes remains, as a\nwell as reduced ability to recognize small liposomes. c) Precision vs Recall plot for the model\nvalidation at varied confidence thresholds.\n“A second model for phenotype identification was trained using the previously described\npipeline with experimental data from synthetic cell module integration and negative controls.\nThe dataset consisted of 42 JPEG images of 512 by 512 pixels that included all the channels\n10\nfrom the original files. Six different classes of objects were created to include the distinct\nphenotypes observed in fully active synthetic cells: From classes 1 to 6 in the order Replication,\nReplication condensate, Integration, Integration condensate, No phenotype, and PLsyn. In total,\n4,469 liposomes were labelled in the samples. The training subset contained 824 class 1, 74\nclass 2, 228 class 3, 24 class 4, 2,224 class 5, and 628 class 6. Training was performed using the\nsame parameters as for yolov7-seg. The resulting model was tested against the validation\ndataset that included 467 liposomes. We confirmed the algorithm is capable of identifying all\nphenotypes it was trained on, but it goes through faster decrease of precision upon reduction\nof confidence, compared to YOLOv7-seg. In addition, the maximum recall achieved was 79% at\na 50% confidence threshold (Supplementary Fig. 14). However, for categories with reduced\nrepresentation in the training dataset, the model is prone to miss- or fail to identify objects.”\nFig. S14. Deep learning-assisted phenotype classification. a) Precision vs Recall plot for the\nmodel validation at varied confidence thresholds. b) YOLO detection output with 80%\nconfidence.\nWe summarized the main findings in the revised manuscript, line 219, as: “For better\ngeneralization of image analysis across experiments and sample types, we developed a deep-\nlearning-based method for automated vesicle segmentation and phenotype classification. The\nmodel was able to identify 306 liposomes with 90% confidence out of 353 manually identified\nvesicles (Supplementary Fig. 13). Moreover, the model enabled classification of the phenotypes,\nincluding vesicles with localized replication foci, without relying on fluorescence intensity\nthresholding or other predefined parameters (Supplementary Fig. 14).”\nIn terms of implementation, while the availability of the SMELDit code is appreciated, the fact\n11\nthat it relies on MATLAB—an expensive, engineer-oriented commercial platform—limits\naccessibility and reuse, especially among researchers in the life sciences. Reimplementing the\npipeline using an open-source, community-supported language such as Python (e.g., with\nnapari, or PyTorch) or Java (e.g., Fiji/ImageJ) would significantly increase its utility and\nadoption.\nReply: We agree with the reviewer and converted the SMELDit code to Python: pySMELDit.\nTechnical note: The MATLAB function called imfill which we used for processing grayscale\nimages has no disclosed algorithm in the Mathworks documentation. Therefore, when\nconverting the code to Python, we had to approximate its function with\nskimage.morphology.reconstruction which performs similarly, but not exactly the same.\nTherefore, the results obtained with the MATLAB and Python versions may not be identical\nsince the liposome recognition function is slightly different.\nWe made the complete code of pySMELDit with example image (‘test sample’) available on\nGitHub repository and provided the new link in the Code Availability statement.\nFinally, since the GitHub repository does not include example datasets or segmentation outputs\ndue to file size constraints, I encourage the authors to provide at least one representative\ndataset—including raw images, segmentation masks, and extracted features—to support\nreproducibility and allow others to test and extend the tool.\nReply: We provided a TestCase image. Instructions on how to run the code can be found in the\nREADME file. We updated the nr-software policy accordingly.\nHere is what it looks like to run:\nby integrating genome-driven replication and membrane biosynthesis within liposomes. The\nimaging data is central to the conclusions of the manuscript. Incorporating more advanced\ncomputational methods for segmentation, feature extraction, and phenotype classification\nwould enhance the reproducibility and interpretability of the analysis, ultimately increasing\nthe impact of the work.\nReviewer #2 (Remarks on code availability):\nThe authors provide a link to their image analysis code SMELDit on GitHub\n(https://github.com/DanelonLab/SMELDit), which includes a README file with basic usage\ninstructions. The documentation outlines the general pipeline and required input files.\nI have not attempted to install or run the code, as no example datasets or sample images are\ncurrently provided. The repository states that image data and segmentation outputs are not\nincluded due to size limitations. I have requested that the authors provide a representative\ndataset to facilitate testing and reproducibility.\nIn its current state, the code appears potentially useful for the community, but usability and\nreproducibility are limited by the absence of test data and by the reliance on MATLAB, which is\na commercial platform not easily accessible to all researchers. Porting the tool to an open-\nsource language such as Python or Java would make it significantly more accessible and\n13\nreusable.\nReply: We fully engaged with the reviewer’s suggestions and provided two versions of\nSMELDit, one on MATLAB and one on Python. We also provided a representative dataset to\nfacilitate testing.\nReviewer #3 (Remarks to the Author):\nSierra et al. describe the integration of two fundamental modules likely required to achieve the\ndevelopment of a synthetic cell, namely DNA replication and lipid synthesis (metabolism). They\ndemonstrate that by combining PURE with a linear template encoding 6 proteins (2 required\nfor DNA replication, and 4 enzymes required for lipid synthesis) and encapsulating the reaction\nin liposomes. Using FACS and direct imaging of these liposomes, they were able to show that\nboth DNA replication and lipid synthesis occur in these liposomes, although the co-occurrence\nof these two processes was a surprisingly rare event compared to each module alone. The FACS\ndata has some shortcomings in regards to demonstrating that DNA replication occurred, although\nthese shortcomings are for the most part resolved with the direct imaging approach. Although it\nmight be worthwhile to consider conducting a time-course experiment if possible. Overall, the\nmanuscript is well written, and for the most part well supported by data (with the exception of\nFig 2 and corresponding Supp figures). The authors should address these issues in a revised\ndocument. Overall, this manuscript demonstrates a significant step forward which will be of\ngeneral interest to the field.\nReply: We thank the reviewer for the overall positive evaluation and constructive comments.\nIn the revised manuscript, we conducted a time-course confocal imaging experiment and\naddressed the reviewer’s issue about Fig. 2 and the associated Supplementary figures.\nSpecific comments:\nThere appears to be a copy – paste mistake in Figure S5 (page 6) as the two figures in the last\ntwo rows – middle column are identical.\nReply: The reviewer is correct; we made a mistake when pasting one of the graphs. This is\ncorrected.\nAlthough PS synthesis seems to be fairly robust and relatively clearly visible based on the FACS\ndata provided in Figure S5, whether DNA replication was functional is much less clear /\napparent. For example, the seemingly most obvious DNA replication event occurred in replicate\n2 at 34C, but the confounding factor is the fact that the positive control data points are in\ngeneral about one order of magnitude higher than the negative control. Could this be due to\n14\nsimply having generally higher DNA concentrations from the beginning in the positive control\nliposomes, or is the expectation that all liposomes are expected to perform DNA replication.\nThe analysis on the other hand (using a threshold) suggests that the expectation is that only a\nfraction of liposomes will be able to perform functional DNA replication.\nReply: Yes, only a fraction of liposomes was able to perform functional DNA replication. The\npercentage of liposomes that perform replication corresponds to % in ROI1 + ROI2 = 5.9 + 11.8\n= 17.7% for replicate 2 at 34 °C. Positive data points are not one order of magnitude higher\nthan the negative control. We noticed that in some graphs the y-axis labels were slightly shifted\nrelative to the ticks; this may have been misleading. This layout issue is fixed.\nIn Fig. S5, negative control samples correspond to time-zero samples for each condition. For\nclarity we added a sentence in the legend of the figure. Therefore, DNA concentration from the\nbeginning cannot be higher in the positive control liposomes. Moreover, we verified that the\ninitial amount of DNA in positive samples did not lead to a higher dsGreen signal in the absence\nof replication. This can be seen in Fig. S21 (formerly S19) when comparing the dsGreen\nintensity at time zero (with DNA but no replication) and in the no-DNA control reaction (Fig.\nS5). In the course of replication, a fraction of liposomes exhibits dsGreen signal that exceeds the\nintensity threshold (Fig. S21a). This result clearly shows that DNA replication is functional in\nsome liposomes (ROI1 + ROI2). Further support is provided with microscopy data.\nFurthermore, in replicate 6 at 34C it appears that the threshold for the NC and PC is not the\nsame!\nReply: The thresholds for both conditions were the same but the y-axis labels were slightly\nshifted from the ticks. We corrected it.\nIn general, the evidence for DNA replication based on Figure S5 seems relatively weak. Would it\nbe possible to perform a FACS analysis of the same samples both at T=0 and at 16 hours, by\nsubjecting a fraction of the reaction to FACS at the beginning of the reaction. This would\nprovide a good baseline for identifying whether the liposome populations shift in the FACS\nmeasurement relative to T0.\nReply: The experiment suggested by the reviewer was shown in Fig. S19 (now S21) and\ndiscussed on page 9. The same samples were analyzed by FACS at different time points, from\nT=0 to 16 hours, and a clear shift of the data points could be measured over time with the\nDNArep module (Fig. S21). Fluorescence intensity thresholds were defined at T=0 hour and the\npercentage of gated events in both dsGreen (DNA replication reporter) and mCherry (lipid\nsynthesis reporter) channels were calculated. We think that the results show strong evidences\nof DNA replication in ~2-10% of liposomes containing the full DNArep-PLsyn genome and in\n~10-20% of the liposomes containing only the DNArep module after 4-8 hours.\nbelow).\nThe data shown in Fig 2e should be supported by a statistical test to indicate whether or not\nthere are indeed significant differences in DNA replication observed.\nReply: We performed a statistical test in Fig. 2e and Fig. 5f as described in the figure legends:\n“DNA concentration changes between 0 h and 16 h were assessed using a log-ratio paired t-\ntest. Log-transformed ratios of 16-h to 0-h values were calculated for each replicate and each\ncorresponding gene (pssA and p2). A one-sample t-test was then performed to determine if the\nmean log-ratio significantly differed from zero (p < 0.05). * p ≤ 0.05, ** p ≤ 0.01, and *** p ≤\n0.001.”\nThe differences in DNA concentrations between the 0-h and 16-h samples were statistically\nsignificant (p < 0.05). The p value is the lowest at 34 °C suggesting more efficient DNA\nreplication. We have not run a statistical test to compare the amplification between the three\ntemperatures.\nSince this qPCR is a bulk assay it likely can’t exclude the possibility that DNA replication, if it\noccurs, actually occurs inside liposomes or outside of liposomes (liposomes could burst and\nrelease their content and DNA replication could occur in the supernatant)?\nReply: We excluded the possibility that DNA replication and gene expression occurred outside\nliposomes by the external addition of DNase. Nonencapsulated DNA or DNA released from\nbursting liposomes would be degraded by DNase. We then inactivated DNAse before qPCR\nmeasurements to allow for the quantification of the internal DNA released from bursting\nliposomes. Note that in qPCR experiments, the negative control sample is from T=0 hour.\nTherefore, the measured DNA concentration corresponds to the amount of input, encapsulated\nDNA.\nMoreover, we confirmed that DNA replication occurred inside liposomes by confocal\nmicroscopy. When adding the intercalating dye dsGreen in liposome samples, no fluorescence\nsignal was detected outside liposomes, only inside liposomes.\nDirect imaging of liposomes as shown in Fig 3 is more convincing than the FACS data in regards\nto DNA replication, although here it would be good to provide a time-course as well (image\nliposomes at T=0 and then repeat imagine at reasonable intervals of every few hours). The\nfollowing experiments in Fig 4 and 5 also nicely add to the evidence that DNA replication\nindeed occurs in liposomes. Could it therefore be that FACS is maybe not an ideal method for\ncharacterizing these liposomes as they could rupture and disintegrate in the FACS for example?\nReply: Thank you for suggesting to directly image liposomes over time. We performed a time-\ncourse experiment to complement the FACS data shown in Fig. S21. The results are provided in\nthe new Supplementary Fig. 26. and confirm the increasing number of liposomes exhibiting\nreplication separately or concurrently with lipid synthesis.\nenables extraction of spatial features. On the other hand, FACS provides high-throughput\nanalysis of liposome samples and its utilization is easier. We therefore decided to combine both\ntechniques. We cannot rule out the possibility that samples get damaged on FACS fluidics or\nsample handling (filtering step). However, we expect this would similarly affect liposomes\nregardless of their activity states, and thus does not influence phenotypic mapping.\nIt remains somewhat curious why such a low fraction of liposomes are exhibiting DNA\nreplication as well as liposome synthesis…\nReply: We carefully investigated this point in the revised manuscript and performed additional\nexperiments that showed that a higher proportion of functional synthetic cells can be produced\nby improving the quality and purity of the template DNA.\nIn the Discussion section, we added: “We suspected that the robustness of the system may be\nlimited by the DNA quality and purity. Using a stable source of DNA, namely a sequence-\nverified circular plasmid to generate the linear DNArep-PLsyn template, improved the system’s\nefficiency over a factor of 3.5 with ~30% of fully functional synthetic cells. We attribute this\nimprovement to a higher purity of the full-length DNA that also enables to work with increased\nconcentrations of active template. The remaining fraction of nonfunctional synthetic cells\nprobably arises from deficient molecular compositions inside vesicles, which cannot support all\nfunctions concurrently. For instance, differences in the loading of DNA or translation\nmachinery between liposomes probably lead to varying levels of expressed genes, while\nuneven supply of substrates or cofactors for DNArep and PLsyn would result in varying outputs\nof the modules. Even if it is clear that prototype synthetic cells will not have the level of control\nand robustness of natural living cells, a major challenge will be to mitigate this heterogeneity\nand increase the proportion of active vesicles.”\nMoreover, in the Results section, line 331, we added a paragraph and included four new\nsupplementary figures reporting these findings:\n“High amounts of the PCR-amplified genome could be obtained, allowing for nanomolar input\nconcentrations. Its expression in liposomes improved the robustness and efficiency of the\nsystem, yielding ~30% (vs 8%) of fully active synthetic cells and over 37% (vs 17%) with at\nleast one functional module, while reducing variability between replicates (Supplementary\nFigs. 23-25). The change of phenotype map compared to Fig. 3c (template obtained by\noverlapping PCR) suggests an increased ability of DNArep-active liposomes to also produce\nphospholipids.\n17"}]

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