Biology subjects

Yoder, L.

Publications and source records attributed to Yoder, L..

4 recordsLinked to original sources

bioRxiv2024

Intrinsic molecular identifiers enable robust molecular counting in single-cell sequencing

Particle-templated instant partition sequencing (PIPseq), is an emerging approach for massively scalable single-cell gene expression studies that does not require complex instrumentation or expensive consumables. We present PIPseqTM V, a novel implementation of the PIPseq workflow with significant improvements in assay performance and sensitivity compared to prior methods. Among the innovations driving the improved performance in PIPseq V is a new approach for transcript counting using Intrinsic Molecular Identifiers (IMIs) from the captured transcript sequence, eliminating the need for traditional Unique Molecular Identifiers (UMIs). IMIs are the starting positions for molecules generated by random fragmentation after limited cycle PCR, which can be used to uniquely identify individual transcript copies of genes expressed within individual cells. To correct for experimental variation in IMI generation, we have developed a dynamic correction strategy that can adapt to different sample cell input, transcript expression, and sequencing depth without the need for external benchmarking or controls. Our results demonstrate that PIPseq V with IMI-based analysis provides biological information comparable to established UMI-based approaches while avoiding UMI-associated biases. Dynamic correction offers a robust and data-driven analysis strategy for scRNAseq.

genomics↗

bioRxiv2020

Neural Flip-Flops III: Stomatogastric Ganglion

The stomatogastric ganglion (STG) is a group of about 30 neurons that resides on the stomach in decapod crustaceans. Its two central pattern generators (CPGs) control the chewing action of the gastric mill and the peristaltic movement of food through the pylorus to the gut. The STG has been studied extensively because it has properties that are common to all nervous systems and because of the small number of neurons and other features that make it convenient to study. So many details are known that the STG is considered a classic test case in neuroscience for the reductionist strategy of explaining the emergence of macro-level phenomena from micro-level data. In spite of the intense scrutiny the STG has received, how it generates its rhythmic patterns of bursts remains unknown. The explicit neural networks proposed here model the pyloric CPG of the American lobster (Homarus americanus). The models share enough significant features with the lobsters CPG that they may be considered first approximations, or perhaps simplified versions, of STG architecture. The similarities include 1) mostly inhibitory synapses; 2) pairs of cells with reciprocal inhibitory inputs, complementary outputs that are approximately 180 degrees out of phase, and state changes occurring with the high output changing first; 3) cells that have reciprocal, inhibitory inputs with more than one other cell; and 4) six cells that produce coordinated oscillations with the same period, four phases distributed approximately uniformly over the period, and half of the burst durations approximately 1/4 of the period and the other half 3/8. Each models connectivity is explicit, and its operation depends only on minimal neuron capabilities of excitation and inhibition. One model performs a function that fills a gap in standard ring oscillators. It is apparently new to engineering, making it an example of neuroscience and logic circuit design informing each other. Some models are derived from standard circuit designs by moving each negation symbol from one end of a connection to the other. This does not change the logic of the network, but it changes each logic gate to one that can be implemented with a single neuron.

neuroscience↗

bioRxiv2020

Neural Flip-Flops II: Short-Term Memory and Electroencephalography

By enabling many brain structures state changes, the explicit cascaded oscillators proposed here can generate the rhythmic neural activity found in EEGs. The function of such synchronization in information processing systems is timing error avoidance. The narrow requirement for the oscillator input pulse duration suggests a possible relationship to the abnormal electrical activity characteristic of epileptic seizures. Together, flip-flops and synchronization by oscillators suggest a resolution to the longstanding controversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts. The proposed cascade of oscillators consists of a ring oscillator and four toggle flip-flops connected in sequence. The novel oscillator and toggle are composed of three and six neurons, respectively. Their operation depends only on minimal properties of excitatory and inhibitory inputs. The hypothesis that cascaded oscillators produce EEG phenomena implies that the distribution of EEG frequencies is determined by just two parameters, the mean (d) and standard deviation ({sigma}d) of the delay times of neurons that make up the initial oscillators in the cascades. For example, if d and {sigma}d are measured in milliseconds, the boundary separating the alpha and beta frequency bands is O_FD O_INLINEFIG[Formula]C_INLINEFIGC_FD With 4 and 1.5 ms being the best available estimates for d and {sigma}d, respectively, this predicted boundary value is 14.9 Hz, which is within the range of commonly cited estimates obtained empirically from EEGs. Four and 1.5 ms also accurately predict the peaks and other boundaries of the five major EEG frequency bands. Significance statementThe neuronal model proposed here implies several major aspects of electroencephalography. The matched periods of neural activity found in EEGs, and their wide distribution across the brain and across the frequency spectrum, follow from selective pressure for a biologically useful function: timing error avoidance for diverse brain functions in the trade-off between speed and accuracy. This activity can be achieved with a simple organization of synaptic connections and minimal neuron capabilities of excitation and inhibition. The multimodal distribution of EEG frequencies is an explicit function of the mean and variance of neuron delay times. The model suggests a relationship to epileptic seizures and a resolution to a short-term memory controversy. Two EEG characteristics make other models implausible.

neuroscience↗

bioRxiv2018

Explicit neural models for flip-flops and oscillators generate phenomena of short-term memory and electroencephalograms

The networks proposed here show how neurons can be connected to form flip-flops, the basic building blocks in sequential logic systems. The novel neural flip-flops (NFFs) are explicit, dynamic, and can generate known phenomena of short-term memory. For each network design, all neurons, connections, and types of synapses are shown explicitly. The neurons operation depends only on explicitly stated, minimal properties of excitement and inhibition. This operation is dynamic in the sense that the level of neuron activity is the only cellular change, making the NFFs operation consistent with the speed of most brain functions. Memory tests have shown that certain neurons fire continuously at a high frequency while information is held in short-term memory. These neurons exhibit seven characteristics associated with memory formation, retention, retrieval, termination, and errors. One of the neurons in each of the NFFs produces all of the characteristics. This neuron and a second neighboring neuron together predict eight unknown phenomena. These predictions can be tested by the same methods that led to the discovery of the first seven phenomena. NFFs, together with a decoder from a previous paper, suggest a resolution to the longstanding controversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts. Two novel NFFs are composed of two and four neurons. Their designs follow directly from a standard electronic flip-flop design by moving each negation symbol from one end of the connection to the other. This does not affect the logic of the network, but it changes the logic of each component to a logic function that can be implemented by a single neuron. This transformation is reversible and is apparently new to engineering as well as neuroscience.

neuroscience↗