Nature as blueprint: Global phenotype engineering of CHO production cells based on a multi-omics comparison with plasma cells.

Especially for the production of artificial, difficult to express molecules a further development of the CHO production cell line is required to keep pace with the continuously increasing demands. However, the identification of novel targets for cell line engineering to improve CHO cells is a time and cost intensive process. Since plasma cells are evolutionary optimized for a high antibody expression in mammals, we performed a comprehensive multi-omics comparison between CHO and plasma cells to exploit optimized cellular production traits. Comparing the transcriptome, proteome, miRNome, surfaceome and secretome of both cell lines identified key differences including 392 potential overexpression targets for CHO cell engineering categorized in 15 functional classes like transcription factors, protein processing or secretory pathway. In addition, 3 protein classes including 209 potential knock-down/out targets for CHO engineering were determined likely to affect aggregation or proteolysis. For production phenotype engineering, several of these novel targets were successfully applied to transient and transposase mediated overexpression or knock-down strategies to efficiently improve productivity of CHO cells. Thus, substantial improvement of CHO productivity was achieved by taking nature as a blueprint for cell line engineering.

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations.

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.

Fluctuation-response relations for integrate-and-fire models with an absolute refractory period.

We study the problem of relating the spontaneous fluctuations of a stochastic integrate-and-fire (IF) model to the response of the instantaneous firing rate to time-dependent stimulation if the IF model is endowed with a non-vanishing refractory period and a finite (stereotypical) spike shape. This seemingly harmless addition to the model is shown to complicate the analysis put forward by Lindner Phys. Rev. Lett. (2022), i.e., the incorporation of the reset into the model equation, the Rice-like averaging of the stochastic differential equation, and the application of the Furutsu-Novikov theorem. We derive a still exact (although more complicated) fluctuation-response relation (FRR) for an IF model with refractory state and a white Gaussian background noise. We also briefly discuss an approximation for the case of a colored Gaussian noise and conclude with a summary and outlook on open problems.

In pursuit of a minimal CHO genome: Establishment of large-scale genome deletions.

Chinese hamster ovary (CHO) cells are the most commonly used mammalian cell line for the production of complex therapeutic glycoproteins. As CHO cells have evolved as part of a multicellular organism, they harbor many cellular functions irrelevant for their application as production hosts in industrial bioprocesses. Consequently, CHO cells have been the target for numerous genetic engineering efforts in the past, but a tailored host cell chassis holistically optimized for its specific task in a bioreactor is still missing. While the concept of genome reduction has already been successfully applied to bacterial production cells, attempts to create higher eukaryotic production hosts exhibiting reduced genomes have not been reported yet. Here, we present the establishment and application of a large-scale genome deletion strategy for targeted excision of large genomic regions in CHO cells. We demonstrate the feasibility of genome reduction in CHO cells using optimized CRISPR/Cas9 based experimental protocols targeting large non-essential genomic regions with high efficiency. Achieved genome deletions of non-essential genetic regions did not introduce negative effects on bioprocess relevant parameters, although we conducted the largest reported genomic excision of 864 kilobase pairs in CHO cells so far. The concept presented serves as a directive to accelerate the development of a significantly genome-reduced CHO host cell chassis paving the way for a next generation of CHO cell factories.

Detecting a periodic signal by a population of spiking neurons in the weakly nonlinear response regime.

Motivated by experimental observations, we investigate a variant of the cocktail party problem: the detection of a weak periodic stimulus in the presence of fluctuations and another periodic stimulus which is stronger than the periodic signal to be detected. Specifically, we study the response of a population of stochastic leaky integrate-and-fire (LIF) neurons to two periodic signals and focus in particular on the question, whether the presence of one of the stimuli can be detected from the population activity. As a detection criterion, we use a simple threshold-crossing of the population activity over a certain time window. We show by means of the receiver operating characteristics (ROC) that the detectability depends only weakly on the time window of observation but rather strongly on the stimulus amplitude. Counterintuitively, the detection of the weak periodic signal can be facilitated by the presence of a strong periodic input current depending on the frequencies of the two signals and on the dynamical regime in which the neurons operate. Beside numerical simulations of the model, we present an analytical approximation for the ROC curve that is based on the weakly nonlinear response theory for a stochastic LIF neuron.

An integrate-and-fire approach to Ca2+ signaling. Part II: Cumulative refractoriness.

Inositol 1,4,5-trisphosphate-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. The agonist concentration stimulating Ca2+ signals is encoded in the frequency of a Ca2+ concentration spike sequence. When a cell is stimulated, the interspike intervals (ISIs) often show a distinct transient during which they gradually increase, a system property we refer to as cumulative refractoriness. We extend a previously published stochastic model to include the Ca2+ concentration in the intracellular Ca2+ store as a slow adaptation variable. This model can reproduce both stationary and transient statistics of experimentally observed ISI sequences. We derive approximate expressions for the mean and coefficient of variation of the stationary ISIs. We also consider the response to the onset of a constant stimulus and estimate the length of the transient and the strength of the adaptation of the ISI. We show that the adaptation sets the coefficient of variation in agreement with current ideas derived from experiments. Moreover, we explain why, despite a pronounced transient behavior, ISI correlations can be weak, as often observed in experiments. Finally, we fit our model to reproduce the transient statistics of experimentally observed ISI sequences in stimulated HEK cells. The fitted model is able to qualitatively reproduce the relationship between the stationary interval correlations and the number of transient intervals, as well as the strength of the ISI adaptation. We also find positive correlations in the experimental sequence that cannot be explained by our model.

Droplet digital PCR: A comprehensive tool for genetic analysis and prediction of bispecific antibody assembly during cell line development.

Molecular biological methods have emerged as inevitable tools to accompany the process of cell line development for the generation of stable and highly productive manufacturing cell lines in the biopharmaceutical industry. PCR-based methods are especially useful for screening and characterization of cell lines due to their low cost, scalability, precision and propensity for multidimensional read-outs. In this study, the diverse applications of droplet digital PCR (ddPCR) as a molecular biological tool for cell line development are demonstrated. Specifically, it is shown that ddPCR can be used to enable precise, sensitive and reproducible absolute quantification of genomically integrated transgene copies during cell line development and cell bank characterization. Additionally, an amplitude multiplexing approach is applied to simultaneously run multiple assays on different genetic targets in a single reaction and advance clonal screening by measuring gene expression profiles to predict the assembly and homogeneity of difficult-to-express (DTE) proteins. The implementation of ddPCR-based assays during cell line development allows for early screening at a transcriptional level, particularly for complex, multidomain proteins, where balanced polypeptide chain ratios are of primary importance. Moreover, it is demonstrated that ddPCR-based genomic characterization improves the robustness, efficiency and comparability of absolute transgene copy number quantification, an essential genetic parameter that must be demonstrated to regulatory authorities during clinical trial and market authorization application submissions to support genetic stability and consistency of the selected cell substrate.

A universal description of stochastic oscillators.

Many systems in physics, chemistry, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example, linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins, the phenomenology of random oscillations can be strikingly similar. Here, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function [Formula: see text](x) that greatly simplifies and unifies the mathematical description of the oscillator's spontaneous activity, its response to an external time-dependent perturbation, and the correlation statistics of different oscillators that are weakly coupled. The function [Formula: see text] (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = µ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width µ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable, provides simple characteristics for the coherence of the random oscillation, and gives a framework for the description of weakly coupled oscillators.

Modeling IP3-induced Ca2+ signaling based on its interspike interval statistics.

Inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. Recent research demonstrated randomness of Ca2+ signaling on all structural levels. We compile eight general properties of Ca2+ spiking common to all cell types investigated and suggest a theory of Ca2+ spiking starting from the random behavior of IP3 receptor channel clusters mediating the release of Ca2+ from the endoplasmic reticulum capturing all general properties and pathway-specific behavior. Spike generation begins after the absolute refractory period of the previous spike. According to its hierarchical spreading from initiating channel openings to cell level, we describe it as a first passage process from none to all clusters open while the cell recovers from the inhibition which terminated the previous spike. Our theory reproduces the exponential stimulation response relation of the average interspike interval Tav and its robustness properties, random spike timing with a linear moment relation between Tav and the interspike interval SD and its robustness properties, sensitive dependency of Tav on diffusion properties, and nonoscillatory local dynamics. We explain large cell variability of Tav observed in experiments by variability of channel cluster coupling by Ca2+-induced Ca2+ release, the number of clusters, and IP3 pathway component expression levels. We predict the relation between puff probability and agonist concentration and [IP3] and agonist concentration. Differences of spike behavior between cell types and stimulating agonists are explained by the different types of negative feedback terminating spikes. In summary, the hierarchical random character of spike generation explains all of the identified general properties.

Theory of the asynchronous state of structured rotator networks and its application to recurrent networks of excitatory and inhibitory units.

Recurrently coupled oscillators that are sufficiently heterogeneous and/or randomly coupled can show an asynchronous activity in which there are no significant correlations among the units of the network. The asynchronous state can nevertheless exhibit a rich temporal correlation statistics that is generally difficult to capture theoretically. For randomly coupled rotator networks, it is possible to derive differential equations that determine the autocorrelation functions of the network noise and of the single elements in the network. So far, the theory has been restricted to statistically homogeneous networks, making it difficult to apply this framework to real-world networks, which are structured with respect to the properties of the single units and their connectivity. A particularly striking case are neural networks for which one has to distinguish between excitatory and inhibitory neurons, which drive their target neurons towards or away from the firing threshold. To take into account network structures like that, here we extend the theory for rotator networks to the case of multiple populations. Specifically, we derive a system of differential equations that govern the self-consistent autocorrelation functions of the network fluctuations in the respective populations. We then apply this general theory to the special but important case of recurrent networks of excitatory and inhibitory units in the balanced case and compare our theory to numerical simulations. We inspect the effect of the network structure on the noise statistics by comparing our results to the case of an equivalent homogeneous network devoid of internal structure. Our results show that structured connectivity and heterogeneity of the oscillator type can both enhance or reduce the overall strength of the generated network noise and shape its temporal correlations.

Active Brownian particles in a biased periodic potential.

We study transport properties of an active Brownian particle with an Rayleigh-Helmholtz friction function in a biased periodic potential. In the absence of noise and depending on the parameters of the friction function and on the bias force, the motion of the particle can be in a locked state or in different running states. According to the type of solutions, the parameter plane of friction and bias force can be divided into four regions. In these different regimes, there is either only a locked state, only a running state, a bistability between locked and running states, or a bistability of two different running states (corresponding to a systematic motion to the left or right, respectively). In the presence of noise, the mean velocity depends in different ways on the noise intensity for the various parameter regimes. These dependences are explored by means of numerical simulations and simple analytical estimates for limiting cases.

Deciphering integration loci of CHO manufacturing cell lines using long read nanopore sequencing.

Despite advances in genetic characterization of Chinese hamster ovary (CHO) cell lines regarding identification of integration sites using next generation sequencing, e.g. targeted locus amplification sequencing (TLA-seq), the concatemer structure of the integrated vectors remains elusive. Here, the entire integration locus of two CHO manufacturing cell lines was reconstructed combining CRISPR/Cas9 target enrichment, nanopore sequencing and the Canu de novo assembly pipeline. An IgG producing CHO cell line integrated 3 vector copies, which were near full-length and contained all relevant vector elements such as transgenes and their promoters on each of the vector copies. In contrast, a second CHO cell line producing a bivalent bispecific antibody integrated 7 highly fragmented vector copies in different orientations leading to head-to-head and tail-to-tail fusions. The size of the vector fragments ranged from 3.0 to 11.4 kbp each carrying 1-3 transgenes. The breakpoints of the genome-vector and vector-vector junctions were validated using Sanger sequencing and Southern blotting. A comparison to TLA-seq data confirmed the genomic breakpoints, but most of the breakpoints of the vector-vector fusions were missed by TLA-seq. For the first time, the complete transgene locus of CHO manufacturing cell lines could be deciphered. Strikingly, the application of the nanopore long-read sequencing technology led to novel insights into the complexity of genomic transgene integrations of CHO manufacturing cell lines generated via random integration.

An integrate-and-fire approach to Ca2+ signaling. Part I: Renewal model.

In computational neuroscience integrate-and-fire models capture the spike generation by a subthreshold dynamics supplemented by a simple fire-and-reset rule; they allow for a numerically efficient and analytically tractable description of stochastic single cell as well as network dynamics. Stochastic spiking is also a prominent feature of Ca2+ signaling which suggests to adopt the integrate-and-fire approach for this fundamental biophysical process. The model introduced here consists of two components describing 1) activity of clusters of inositol-trisphosphate receptor channels and 2) dynamics of the global Ca2+ concentrations in the cytosol. The cluster dynamics is given in terms of a cyclic Markov chain, capturing the puff, i.e., the punctuated release of Ca2+ from intracellular stores. The cytosolic Ca2+ concentration is described by an integrate-and-fire dynamics driven by the puff current. For the cyclic Markov chain we derive expressions for the statistics of the interpuff interval, the single-puff strength and the puff current assuming constant cytosolic Ca2+. The latter condition is often well approximated because cytosolic Ca2+ varies much slower than the cluster activity does. Furthermore, because the detailed two-component model is numerically expensive to simulate and difficult to treat analytically, we develop an analytical framework to approximate the driving puff current of the stochastic cytosolic Ca2+ dynamics by a temporally uncorrelated Gaussian noise. This approximation reduces our two-component system to an integrate-and-fire model with a nonlinear drift function and a multiplicative Gaussian white noise, a model that is known to generate a renewal spike train, i.e., a point process with statistically independent interspike intervals. The model allows for fast numerical simulations, permits to derive analytical expressions for the rate of Ca2+ spiking and the coefficient of variation of the interspike interval, and to approximate the interspike interval density and the spike train power spectrum. Comparison of these statistics to experimental data is discussed.

The steady state and response to a periodic stimulation of the firing rate for a theta neuron with correlated noise.

The stochastic activity of neurons is caused by various sources of correlated fluctuations and can be described in terms of simplified, yet biophysically grounded, integrate-and-fire models. One paradigmatic model is the quadratic integrate-and-fire model and its equivalent phase description by the theta neuron. Here we study the theta neuron model driven by a correlated Ornstein-Uhlenbeck noise and by periodic stimuli. We apply the matrix-continued-fraction method to the associated Fokker-Planck equation to develop an efficient numerical scheme to determine the stationary firing rate as well as the stimulus-induced modulation of the instantaneous firing rate. For the stationary case, we identify the conditions under which the firing rate decreases or increases by the effect of the colored noise and compare our results to existing analytical approximations for limit cases. For an additional periodic signal we demonstrate how the linear and nonlinear response terms can be computed and report resonant behavior for some of them. We extend the method to the case of two periodic signals, generally with incommensurable frequencies, and present a particular case for which a strong mixed response to both signals is observed, i.e. where the response to the sum of signals differs significantly from the sum of responses to the single signals. We provide Python code for our computational method: https://github.com/jannikfranzen/theta_neuron .

Fluctuation-Dissipation Relations for Spiking Neurons.

Spontaneous fluctuations and stimulus response are essential features of neural functioning, but how they are connected is poorly understood. I derive fluctuation-dissipation relations (FDR) between the spontaneous spike and voltage correlations and the firing rate susceptibility for (i) the leaky integrate-and-fire (IF) model with white noise and (ii) an IF model with arbitrary voltage dependence, an adaptation current, and correlated noise. The FDRs can be used to derive thus far unknown statistics analytically [model (i)] or the otherwise inaccessible intrinsic noise statistics [model (ii)].

Spatially resolved reflectance from turbid media having a rough surface. Part II: experiments.

Spatially resolved reflectance measurements are a standard tool for determining the absorption and scattering properties of turbid media such as biological tissue. However, in literature, it was shown that these measurements are subject to errors when a possible rough surface between the turbid medium and the surrounding is not accounted for. We evaluated these errors by comparing the spatially resolved reflectance measured on rough epoxy-based samples with Monte Carlo simulations using Lambertian surface scattering, the Cook-Torrance model, and the generalized Harvey-Shack model as surface scattering models. To this aim, goniometric measurements on the epoxy-based samples were compared to the angularly resolved reflectance of the three surface models to estimate the corresponding model parameters. Finally, the optical properties of the phantoms were determined using a Monte Carlo model with a smooth surface.

Spatially resolved reflectance from turbid media having a rough surface. Part I: simulations.

Determining the optical properties of turbid media with spatially resolved reflectance measurements is a well-known method in optical metrology. Typically, the surfaces of the investigated materials are assumed to be perfectly smooth. In most realistic cases, though, the surface has a rough topography and scatters light. In this study, we investigated the influence of the Cook-Torrance surface scattering model and the generalized Harvey-Shack surface scattering model on the spatially resolved reflectance based on Monte Carlo simulations. Besides analyzing the spatially resolved reflectance signal, we focused on the influence of surface scattering on the determination of the reduced scattering coefficients and absorption coefficients of turbid media. Both models led to significant errors in the determination of optical properties when roughness was not accounted for.

A self-consistent analytical theory for rotator networks under stochastic forcing: Effects of intrinsic noise and common input.

Despite the incredible complexity of our brains' neural networks, theoretical descriptions of neural dynamics have led to profound insights into possible network states and dynamics. It remains challenging to develop theories that apply to spiking networks and thus allow one to characterize the dynamic properties of biologically more realistic networks. Here, we build on recent work by van Meegen and Lindner who have shown that "rotator networks," while considerably simpler than real spiking networks and, therefore, more amenable to mathematical analysis, still allow one to capture dynamical properties of networks of spiking neurons. This framework can be easily extended to the case where individual units receive uncorrelated stochastic input, which can be interpreted as intrinsic noise. However, the assumptions of the theory do not apply anymore when the input received by the single rotators is strongly correlated among units. As we show, in this case, the network fluctuations become significantly non-Gaussian, which calls for reworking of the theory. Using a cumulant expansion, we develop a self-consistent analytical theory that accounts for the observed non-Gaussian statistics. Our theory provides a starting point for further studies of more general network setups and information transmission properties of these networks.

Information transmission in recurrent networks: Consequences of network noise for synchronous and asynchronous signal encoding.

Information about natural time-dependent stimuli encoded by the sensory periphery or communication between cortical networks may span a large frequency range or be localized to a smaller frequency band. Biological systems have been shown to multiplex such disparate broadband and narrow-band signals and then discriminate them in later populations by employing either an integration (low-pass) or coincidence detection (bandpass) encoding strategy. Analytical expressions have been developed for both encoding methods in feedforward populations of uncoupled neurons and confirm that the integration of a population's output low-pass filters the information, whereas synchronous output encodes less information overall and retains signal information in a selected frequency band. The present study extends the theory to recurrent networks and shows that recurrence may sharpen the synchronous bandpass filter. The frequency of the pass band is significantly influenced by the synaptic strengths, especially for inhibition-dominated networks. Synchronous information transfer is also increased when network models take into account heterogeneity that arises from the stochastic distribution of the synaptic weights.

Influence of Lambertian surface scattering on the spatially resolved reflectance from turbid media: a computational study.

The determination of the optical properties in turbid media plays an essential role in medical diagnostics and process control. The method of spatially resolved reflectance measurements is a frequently used tool to evaluate the reduced scattering coefficient as well as the absorption coefficient. In most cases a smooth interface is assumed between the medium under investigation and the surrounding medium. However, in reality, a rough surface is present at the interface, which alters the light interaction with the surface and volume of the turbid medium. Hence, the idea behind this paper was to investigate the influence of rough surfaces on the spatially resolved reflectance and thus on the determination of the optical properties of turbid media. Particularly, the influence of a Lambertian scattering surface on the result of Monte Carlo simulations of a spatially resolved reflectance setup is shown. In addition, we distinguish between the different interaction modes of surface scattering on the spatially resolved reflectance. There is a strong influence of roughness when the light enters and leaves the turbid medium. Furthermore, the simulations show that, especially for small reduced scattering coefficients and absorption coefficients, large errors in the determination of the optical properties are obtained.