Replication Dynamics

Abstract:

Cell counting is a vital practice in the maintenance and manipulation of cell cultures. It is a crucial aspect of assessing cell viability and determining proliferation rates, which are integral to maintaining the health and functionality of a culture. Additionally, it is critical for establishing the time of infection in bioreactors and monitoring cell culture response to targeted infection over time. However, when cell counting is performed manually, the time involved can become substantial, particularly when multiple cultures need to be handled in parallel. Automated cell counters, which enable significant time reduction, are commercially available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9), were used for training, validation, and testing of the model. The ML model yielded F1 scores of 0.97 and 0.96 for alive and dead cells, respectively, which represents a substantially improved performance over that of other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g. in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting.

Abstract:

Multiplexed single-molecule magnetic tweezers (MT) have recently been employed to probe the RNA synthesis dynamics of RNA-dependent RNA polymerases (RdRp). Here, we present a protocol for simultaneously probing the RNA synthesis dynamics of hundreds of single polymerases with MT. We describe the preparation of a dsRNA construct for probing single RdRp kinetics. We then detail the measurement of RdRp RNA synthesis kinetics using MT. The protocol is suitable for high-throughput probing of RdRp-targeting antiviral compounds for mechanistic function and efficacy. For complete details on the use and execution of this protocol, please refer to Janissen et al. (2021).

Abstract:

Pausing by bacterial RNA polymerase (RNAp) is vital in the recruitment of regulatory factors, RNA folding, and coupled translation. While backtracking and intra-structural isomerization have been proposed to trigger pausing, our mechanistic understanding of backtrack-associated pauses and catalytic recovery remains incomplete. Using high-throughput magnetic tweezers, we examine the Escherichia coli RNAp transcription dynamics over a wide range of forces and NTP concentrations. Dwell-time analysis and stochastic modeling identify, in addition to a short-lived elemental pause, two distinct long-lived backtrack pause states differing in recovery rates. We identify two stochastic sources of transcription heterogeneity: alterations in short-pause frequency that underlies elongation-rate switching, and variations in RNA cleavage rates in long-lived backtrack states. Together with effects of force and Gre factors, we demonstrate that recovery from deep backtracks is governed by intrinsic RNA cleavage rather than diffusional Brownian dynamics. We introduce a consensus mechanistic model that unifies our findings with prior models.