Inferring the immune response from repertoire sequencing

High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences

Precise tracking of vaccine-responding T-cell clones reveals convergent and personalized response in identical twins

T-cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from longitudinal repertoire data. We applied this framework to data from three pairs of identical twins immunized with the yellow fever vaccine. We identified 500-1500 responding TCRs in each donor and validated them using three independent assays. While the responding TCRs were mostly private, albeit with higher overlap between twins, they could be well predicted using a classifier based on sequence similarity. Our method can also be applied to samples obtained post-infection, making it suitable for systematic discovery of new infection-specific TCRs in the clinic

The qualitative shape of voltage response depends on <i>Q</i>_<i>L</i>.

<p>Here we classify the current-to-voltage filter shapes shown as colored solid lines in (a), (b), and (c), which show the three <i>Q</i>_<i>L</i>-regimes with respective examples for <i>Q</i>_<i>L</i> = 0.1, 0.75, 10. In each plot, the high pass component of the voltage response is shown as the colored dashed lines, one for each of three representative values of its characteristic frequency, <i>ω</i>_<i>L</i>τ_<i>w</i> = 10² > γ(blue), <i>ω</i>_<i>L</i>τ_<i>w</i> = 1(green), and <i>ω</i>_<i>L</i>τ_<i>w</i> = 10⁻² < <i>γ</i>⁻¹(red). The solid black line is the low pass component of the voltage response. For the regime shown in (a), the green case can not be achieved when <i>w</i> is hyperpolarizing (<i>g</i> > 0) and the example red case cannot be achieved because it violates the stability condition <i>Q</i>_<i>L</i> < <i>ω</i>_<i>L</i>τ_<i>w</i>.</p

The accessible region of filter shapes depends on <i>Q</i>_<i>L</i> and the relative speed of spiking to intrinsic dynamics <i>ξ</i> = τ_<i>w</i>/τ_<i>c</i>.

<p>The purple region marks the region of voltage resonant filters. This region is contained in the red region of stable filters, whose lower bound moves to larger <i>ν</i>_{<i>ω</i>_<i>L</i>} with <i>Q</i>_<i>L</i>. For relatively slow intrinsic spiking (a, b, c), there are regions of non-spiking resonant(<i>ν</i>_∞ > <i>ν</i>_{<i>ω</i>_<i>L</i>}), but voltage resonant filters. Filters for relatively fast intrinsic dynamics (d, e, f) only exist as high pass resonant filters for large <i>Q</i>_<i>L</i>. (Left to right: , 1.1. Top row: <i>ξ</i> = 10. Bottom row: <i>ξ</i> = 0.1).</p

Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

<div><p>The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and fewer have investigated its effects. To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics, we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model. In the Fokker-Planck approach, the dynamic gain is intractable. The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike. This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input. We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales. We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak. We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance, finding all possible intersections of the three. The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons.</p></div

Correspondence of response between analytical result of no-reset model (blue line) and the numerical result of its EIF version (black circles).

<p>The correspondence holds up to a high frequency cut-off, <i>f</i>_<i>limit</i> (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004636#pcbi.1004636.e207" target="_blank">Eq (54)</a>), due to finite rise time of action potential controlled by Δ_<i>T</i> = 0.35, 0.035. The EIF-version was simulated with <i>V</i>_<i>thr</i> = 1.15, 3, and <i>V</i>_<i>T</i> = 0.8, −1 (the latter was adjusted to keep <i>ν</i>₀ = 2Hz fixed). The black dashed lines correspond to the high frequency limit of the response of the EIF-type model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004636#pcbi.1004636.e206" target="_blank">Eq (53)</a>). The no reset model had the default parameters.</p