{'fraction_non_alphanumeric': 0.03158157533391587, 'fraction_numerical': 0.03763575084259144, 'mean_word_length': 4.822311046511628, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Genome-wide molecular recording using Live-seq

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity in health and disease. However, scRNA-seq requires lysing cells, which makes it impossible to link the individual cells to downstream molecular and phenotypic states. Here, we established Live-seq, an approach for single-cell transcriptome profiling that preserves cell viability during RNA extraction using fluidic force microscopy. Based on cell division, functional responses and whole-cell transcriptome read-outs, we show that Live-seq does not induce major cellular perturbations and therefore can function as a transcriptomic recorder. We demonstrate this recording capacity by preregistering the transcriptomes of individual macrophage-like RAW 264.7 cells that were subsequently subjected to time-lapse imaging after lipopolysaccharide (LPS) exposure. This enabled the unsupervised, genome-wide ranking of genes based on their ability to impact macrophage LPS response heterogeneity, revealing basal NFKBIA expression level and cell cycle state as major phenotypic determinants. Furthermore, we show that Live-seq can be used to sequentially profile the transcriptomes of individual macrophages before and after stimulation with LPS, thus enabling the direct mapping of a cell’s trajectory. Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.


Introduction 41
Most biological processes are inherently transient and dynamic, with cell states changing 42 according to internal programs and/or external stimuli, as illustrated, for example, by the fact 43 that the many different cell types in our body are derived from a single zygote. It is thus critical 44 to not only understand a cell's current state, but also how a cell arrived at that state, i.e. its 45 molecular history, which ensures normal development or causes disease when deviating from 46 its canonical path 1 . However, despite its importance, revealing the history of a cell remains an 47 outstanding technological challenge. Several approaches have been developed with the intent 48 of exploring a cell's past, such as engineered genetic circuits 2 , recombinase-and CRISPR-49 based DNA editors 3-7 and live cell imaging 8,9 . While these methods represent exciting 50 advances, they are limited by their ability to record only one or a few events per single cell 51 rather than providing a comprehensive view of the molecular state of the cell 10 .

53
Single cell transcriptomics (scRNA-seq) is recognized as highly complementary to the above 54 mentioned strategies, as it allows inferring the history and trajectory of cells based on unbiased 55 transcriptomic read-outs [11][12][13][14][15][16][17][18][19] . Such scRNA-seq-based trajectory inference methods are mainly 56 based on the principle that a snapshot of a population of cells at different stages can reflect 57 the molecular changes of an individual cell over time. Although they have already provided 58 numerous biological insights, these trajectory inference tools have a fundamental limitation in 59 that there may be multiple potential dynamics for a measured distribution of cell states that 60 give rise to them [20][21][22] . Thus, the models generated by these tools should be interpreted as

160
To validate the biological relevance of these four clusters, we performed differential gene

204
Together, these results demonstrate that Live-seq enables the accurate stratification of cell 205 types and states similar to conventional scRNA-seq, but without the need to lyse cells.

207
Live-seq preserves cell viability, transcriptome, and growth 208 The analyses described above allowed us to benchmark Live-seq to a widely used 209 conventional, whole cell transcriptome profiling approach (Methods). However, Live-seq was 210 designed to be minimally invasive and to conserve cell viability to allow for continuous

218
These high cell viability numbers suggest that cells are able to quickly recover after a portion 219 of their cytoplasm has been removed. However, assessing cell viability does not inform on 220 putative, short-term molecular effects that Live-seq may impose on targeted cells. To address this, we used standard scRNA-seq (Methods) to profile the individual transcriptomes of IBA 222 cells 1 hour and 4 hours post-cytoplasmic sampling (Figure 3a), while using non-probed cells 223 as negative controls. Quality controls on the cDNA and sequencing libraries (such as mapping 224 rate, number of detected genes) were indistinguishable among the three cell groups (control, 225 1h and 4h post-Live-seq-sampling; Supplementary Figure 3b-c). Remarkably, data analysis 226 revealed no distinct condition-related clusters (Figure 3b), further supported by the 227 observation that only 12 genes were found to be significantly differentially expressed (DE) 228 among the three conditions (Figure 3c). To assess subtle changes in biological processes 229 that may not be visible at the DE level (because of hard cut-offs), we performed a Gene Set 230 Enrichment Analysis (GSEA) on GO biological process terms using the expression levels of 231 all genes, but this did not reveal any specifically enriched processes.

233
These experiments indicate that Live-seq does not induce major, short-term gene expression 234 alterations. To assess putative longer-term phenotypic effects and thus to study the recovery 235 of Live-seq-probed cells in greater detail, we examined the growth dynamics of RAW cells 236 after Live-seq using time-lapse microscopy. We focused on RAW cells for two reasons: i) their 237 size is smallest among the three sampled cell types, and thus their behavior may be most 238 impacted by a cytoplasmic extraction; ii) as semi-adherent cells, their almost spherical shape 239 allowed for a straightforward approximation of cell volume changes over time. Using  Live-seq to record their respective transcriptomes in the ground-state. We then subjected the 262 same cells to LPS treatment and tracked LPS-induced Tnf promoter-driven mCherry 263 expression (hereafter referred to as Tnf-mCherry) using live cell-imaging (Figure 4a). We 264 observed that Tnf-mCherry intensity increased upon LPS treatment, although this intensity 265 varied greatly between RAW cells (Figure 4b), consistent with previous findings 36 . Importantly, 266 no significant difference in the heterogeneity of Tnf-mCherry intensity profiles was observed 267 between cells subjected to Live-seq sampling and those that were not (Figure 4b, two-sided

268
Wilcoxon rank-sum test, P = 0.876). These results provide additional support to the notion that  Table 3). Next, we set out to identify factors that predict both the dynamics 284 and amplitude to which a cell responds to LPS as defined by the rate of Tnf-mCherry intensity 285 increase (slope). Using the Live-seq data, we generated an unsupervised, genome-wide rank 286 of factors according to which extent they impacted a macrophage's ability to respond to LPS.

287
We thereby identified Gelsolin (Gsn) as one of the top negatively correlating factors (R 2 = 0.33,  Table   292 4). This observation seems counterintuitive at first, given the positive correlation between 293 Nfkbia and Tnf expression in conventional snapshot scRNA-seq data (Figure 4f). However, it 294 is in fact consistent with the well-known function of NFKBIA as a key negative regulator of the LPS-NF-κB signaling pathway by suppressing NF-κB, which itself regulates the co-expression 296 of both Nfkbia and Tnf 43 . These findings highlight the complementarity of the Live-seq-based 297 dynamics approach and the scRNA-seq-based snapshot assay. Moreover, while many genes 298 are known to regulate the LPS-NF-κB signaling pathway, including IKKs as positive regulators 299 and A20 as a negative one 43 , the transcriptome-wide read-out enabled by Live-seq clearly 300 points to Nfkbia expression as the strongest negative predictor of LPS-induced Tnf expression.

301
Interestingly, we found that Nfkbia expression is also the most variable among the annotated

309
-0.67). However, this anti-correlation may also reflect that a macrophage's ability to respond 310 to LPS is influenced by its cell cycle phase. To investigate these two plausible scenarios, we 311 correlated the ground-state cell cycle phase of each cell, as inferred from pre-recorded Live-312 seq data, to its corresponding downstream LPS response profile. As shown in Figure 4g, this 313 analysis revealed that cells tend to respond weaker to LPS when they are in S phase (R 2 =  Live-seq sampling approach, which would allow us to record a cell's molecular signature 333 before and after cell state transitioning. To do so, we sampled RAW cells a first time, then 334 stimulated them with LPS, after which we sampled the same cells a second time. In addition, 335 the respective cells were also monitored by time-lapse microscopy after both sampling events 336 (Figure 4j). Out of the 14 cells that were sequentially probed, we obtained transcriptome 337 profiles that passed the quality control cutoff criteria for at least one of the sampling points for

373
The clear advantage of these recently developed approaches, however, is that they support 374 high-throughput, while Live-seq will clearly benefit from further automation procedures that

412
Finally, we demonstrated that Live-seq and conventional scRNA-seq data can be seamlessly 413 integrated with the aim of directly measuring a cell's trajectory (Figure 4l). Indeed, while 414 scRNA-seq provides the accuracy to resolve cellular heterogeneity, Live-seq experimentally 415 maps cell state dynamics. As shown in this study, Live-seq can directly assign cell trajectories 416 by assaying the same cells twice, here, before and after LPS stimulation. Integrating the 417 transcriptomes of these reference cells with scRNA-seq data therefore provides a means to 418 assign trajectories for cells that share the reference transcriptome signature. For such a hybrid 419 approach, the monitoring of relatively few cells with Live-seq proved sufficient, which is readily 420 possible with the current technology. Nonetheless, we expect the next generation of the Live-421 seq approach to allow for sampling of many more cells, including sequentially probed ones.

422
This will in turn provide a future avenue to address some of the long-standing biological 423 questions pertaining to cell dynamics or cellular phenotypic variation 8 .

425
In sum, Live-seq breaks new ground by enabling single cell transcriptome profiling as well as 426 downstream analyses on the same cell at distinct time points (Figure 4). We anticipate that 427 Live-seq will transform single cell transcriptomics from the current end-point type assay into a 428 temporal analysis workflow.

488
ThermoFisher) ) were added. The reverse transcription step was performed in a thermal cycler 489 using the following program: 1) 42 °C for 90 min; 2) 50 °C for 2 min; 3) 42 °C for 2 min, go to 490 step 2 for 4 cycles; 4) 85 °C for 5 min; 5) End. The reverse transcription products were directly  Tn5 (made in-house, 12.5 µM), H2O to 5 µl in total. The tagmentation reaction was assembled on ice, initiated by placing the mix on a thermal cycler at 55 °C for 8 min after which it was 503 kept on ice. Then 1.5 µl SDS (0.2%) was added to inactivate the Tn5 enzyme. PCR reagent 504 mixture was added directly to the tagmented DNA, which contains 1.5 µl dNTP (10 mM each),

523
1× penicillin/streptomycin solution, and 2mM Glutamine. Sampling buffer for preloading was 524 prepared by supplementing a 0.2% solution of Triton-X 100 in nuclease-free water with 2 U 525 μl −1 recombinant RNase inhibitors (Clonetech). Lysis buffer for extract transfer was prepared 526 as that used in the enhanced Smart-seq2 protocol. While cells were maintained at 37°C for 527 time-lapse microscopy before and after extractions, the extraction procedures were all 528 performed at room temperature.

530
The probe reservoir was loaded with 10 µL of mineral oil (Sigma-Aldrich) and a pressure of Δ 531 + 1000 mbar was applied to flow the oil into the microchannel. Once the probe microchannel 532 was filled, the probe was shortly immersed in nuclease-free water, and then kept in air with 533 the residual water carefully blotted off the probe holder with a kimwipe tissue. A 1.0-μl drop of 534 sampling buffer was deposited onto an AG480F AmpliGrid (LTF Labortechnik). The cantilever 535 was introduced into the drop using the micrometer screws to displace the AFM. Once the 536 cantilever was located inside the drop, underpressure (-800 mbar) was applied for the suction of ~0.5 pL of sampling buffer into the probe. The cantilever was then withdrawn from the drop 538 using the micrometer screws.

540
Next, the pre-loaded probe was immersed in the cell sample experimental medium, the cell to 541 be extracted was visualized by light microscopy, and the tip of the FluidFM probe was placed 542 above the cytoplasm of the selected cell. The tip of the probe was then inserted into the 543 cytoplasm through a forward force spectroscopy routine driven by the Z-piezo. The probe was 544 then maintained inside the cell at constant force (500 nN). Underpressure larger than −800 545 mbar was applied to aspirate the cellular content into the probe, whereby the harvested

554
The cantilever was introduced into the drop, and overpressure (more than 1,000 mbar) was 555 applied to release the extract. The microchannel was then rinsed three times by suction and 556 release of lysis buffer into the probe. The 1.0-μl drop was then pipetted into a PCR tube 557 containing an additional 3.2 μL of lysis buffer, and the solution was briefly centrifuged and 558 stored at −80°C until further processing. The entire procedure took approximatively 15 min 559 per extraction, and all steps were monitored in real time by optical microscopy in brightfield.

561
For the alternated human/mouse cell sampling, HeLa and IBA cells were seeded on separate 562 dishes, after which cells were extracted alternatively from one or the other cell type as 563 described above, using the same FluidFM probe. 564 565 LPS-stimulated RAW cells were extracted between 4 and 5 hours after the addition of LPS.

566
For the sequential sampling of the same RAW cell, up to 14 cells were extracted as described     Table 5), and were thus also removed from downstream analyses.

693
Ribosomal protein coding genes were removed as they confound the downstream differential

704
Scaling was applied to all the genes using the function "ScaleData". The scaled data of the

730
The scores of cell cycle phases were calculated using the Seurat function "CellCycleScoring", 731 based on canonical markers 63 .

733
To integrate Live-seq and scRNA-seq data, the canonical correlation analysis (CCA) and 734 mutual nearest neighbors (MNNs)-based approaches embedded in Seurat v3 were used.

735
Specifically, the top 500 highly variable genes of both datasets were chosen for PCA analysis 736 independently. The first 10 PCs of each were used to identify the anchors and for data 737 integration. As another level of control, data stemming from cells belonging to each cluster in 738 both Live-seq and scRNA-seq analyses were collapsed into a "bulk" RNA-seq dataset after 739 which the Pearson's correlation between the "bulk" Live-seq and the "bulk" scRNA-seq

1017
Schematic illustrating the sequential sampling procedure, and time-lapse bright-field and 1018 mCherry fluorescence images of sequential Live-seq sampling (i.e. sequential extractions).

1019
The arrow points to the sequentially sampled cell. (k) tSNE-based visualization of Live-seq 1020 data highlighting the two sequentially sampled cells by a triangle and rectangle, respectively.

1021
A black shape outline represents unstimulated cells, whereas a red shape outline represents