Netanel Ofer | Columbia University (original) (raw)

Papers by Netanel Ofer

Background: Single Nucleotide Polymorphism (SNPs) markers have great potential to identify indivi... more Background: Single Nucleotide Polymorphism (SNPs) markers have great potential to identify individuals, family relations, biogeographical ancestry, and phenotypic traits. In many forensic situations, DNA mixtures of a victim and an unknown suspect exist. Extracting SNP profiles from suspect’s samples can be used to assist investigation or gather intelligence. Computational tools to determine inclusion/exclusion of a known individual from a mixture exist, but no algorithm for extraction of an unknown SNP profile without a list of suspects is available.Results: We present here AH-HA, a novel computational approach for extracting an unknown SNP profile from whole genome sequencing (WGS) of a two persons mixture. AH-HA utilizes techniques similar to the ones used in haplotype phasing. It constructs the inferred genotype as an imperfect mosaic of haplotypes from a reference panel of the target population. It outperforms more simplistic approaches, maintaining high performance through a w...

Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in mos... more Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in most mammalian neurons. Spines, with a bulbous head connected to the dendrite by a thin neck, have a variety of morphologies that likely impact their functional properties. Nevertheless, the question of whether spines belong to distinct morphological subtypes is still open. Addressing this quantitatively requires clear identification and measurements of spine necks. Recent advances in electron microscopy enable large-scale systematic reconstructions of spines with nanometer precision in 3D. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spine structures can be rigorously separated into heads and necks, enabling morphological measurements of spine necks. We then used a database of spine morphological parameters to explore the potential existence of different spine classes. Without exception, our analysis revealed unimodal distributions of individual morphological parameters of spine heads and necks, without evidence for subtypes of spines. The postsynaptic density size was strongly correlated with the spine head volume. The spine neck diameter, but not the neck length, was also correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data reveal a lack of morphological subtypes of spines and indicate that the spine neck length and head volume must be independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.

Nature Neuroscience, 2020

A community-based transcriptomics classification and nomenclature of neocortical cell types To un... more A community-based transcriptomics classification and nomenclature of neocortical cell types To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Neuroinformatics, 2020

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and m... more Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties.
Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal
projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the
characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation
patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the
axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification
schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and
utilizing form-function principles in realistic neuronal reconstructions.

Scientific Reports, 2017

Computational modeling of signal propagation in neurons is critical to our understanding of basic... more Computational modeling of signal propagation in neurons is critical to our understanding of basic principles underlying brain organization and activity. Exploring these models is used to address basic neuroscience questions as well as to gain insights for clinical applications. The seminal Hodgkin Huxley model is a common theoretical framework to study brain activity. It was mainly used to investigate the electrochemical and physical properties of neurons. The influence of neuronal structure on activity patterns was explored, however, the rich dynamics observed in neurons with different morphologies is not yet fully understood. Here, we study signal propagation in fundamental building blocks of neuronal branching trees, unbranched and branched axons. We show how these simple axonal elements can code information on spike trains, and how asymmetric responses can emerge in axonal branching points. This asymmetric phenomenon has been observed experimentally but until now lacked theoretical characterization. Together, our results suggest that axonal morphological parameters are instrumental in activity modulation and information coding. The insights gained from this work lay the ground for better understanding the interplay between function and form in real-world complex systems. It may also supply theoretical basis for the development of novel therapeutic approaches to damaged nervous systems.

BMC Neuroscience, 2013

A. Series of five spikes before failure for a current stimulus of 1.025 mA/cm 2 along the axon, 2... more A. Series of five spikes before failure for a current stimulus of 1.025 mA/cm 2 along the axon, 20μm radius. B. Single spike followed by three failures for a current stimulus of 8.5 mA/cm 2 along the axon, 500μm radius. C. A chaotic response to a current stimulus of 4.064 mA/cm 2 along the axon, 238μm radius.

Neurons generate diverse patterns of activity for various functions. Revealing factors which dete... more Neurons generate diverse patterns of activity for various functions. Revealing factors which
determine neuronal firing patterns is fundamental to a better understanding of brain activity
and coding. Traditionally, the space clamp model has been used to investigate neuronal
electrical activity. In this paper, we study the Hodgkin–Huxley cable model, taking into consideration
axonal geometry. We examine the influence of morphology on neuronal activity,
exploring neuronal response to constant current stimuli injected into one end of fiber-like axons
of different lengths and radii. We demonstrate novel patterns of firing, including a finite
number of spikes and series followed by failures, and under some specific current stimulus
regimes, we also detect irregular behaviors. Our results illustrate various means in which the
pattern of activity may be regulated by axonal structure, suggesting this mechanism is instrumental
in information coding of physiological, as well as deforming pathological conditions.

Drafts by Netanel Ofer

bioRxiv, 2018

Classification of neurons into specific subtypes is essential for better understanding of brain f... more Classification of neurons into specific subtypes is essential for better understanding of brain function and information transmission.
Despite continuous progress, there is still no consensus regarding categorizing neuron taxonomy into proper subtypes.
Current morphology-based classification approaches largely rely on the dendritic tree structure or on the general axonal projection layout.
In this study, we support the use of a morphology-based classification approach, focusing on the axonal tree.
We demonstrate that utilizing the geometrical parameters of axonal tree structures significantly improves neuronal classification compared to the dendritic tree classification.
Furthermore, we used neuronal activity patterns to classify interneurons into subtypes as well.
Simulations of the activity along ramified axonal trees indicate that the axonal branching geometry may yield diverse responses in different subtrees.
The classification schemes introduced here can be utilized to robustly classify neuronal subtypes in a functionally relevant manner. Our results open the door for deducing functionality from anatomical data.

Background: Single Nucleotide Polymorphism (SNPs) markers have great potential to identify indivi... more Background: Single Nucleotide Polymorphism (SNPs) markers have great potential to identify individuals, family relations, biogeographical ancestry, and phenotypic traits. In many forensic situations, DNA mixtures of a victim and an unknown suspect exist. Extracting SNP profiles from suspect’s samples can be used to assist investigation or gather intelligence. Computational tools to determine inclusion/exclusion of a known individual from a mixture exist, but no algorithm for extraction of an unknown SNP profile without a list of suspects is available.Results: We present here AH-HA, a novel computational approach for extracting an unknown SNP profile from whole genome sequencing (WGS) of a two persons mixture. AH-HA utilizes techniques similar to the ones used in haplotype phasing. It constructs the inferred genotype as an imperfect mosaic of haplotypes from a reference panel of the target population. It outperforms more simplistic approaches, maintaining high performance through a w...

Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in mos... more Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in most mammalian neurons. Spines, with a bulbous head connected to the dendrite by a thin neck, have a variety of morphologies that likely impact their functional properties. Nevertheless, the question of whether spines belong to distinct morphological subtypes is still open. Addressing this quantitatively requires clear identification and measurements of spine necks. Recent advances in electron microscopy enable large-scale systematic reconstructions of spines with nanometer precision in 3D. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spine structures can be rigorously separated into heads and necks, enabling morphological measurements of spine necks. We then used a database of spine morphological parameters to explore the potential existence of different spine classes. Without exception, our analysis revealed unimodal distributions of individual morphological parameters of spine heads and necks, without evidence for subtypes of spines. The postsynaptic density size was strongly correlated with the spine head volume. The spine neck diameter, but not the neck length, was also correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data reveal a lack of morphological subtypes of spines and indicate that the spine neck length and head volume must be independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.

Nature Neuroscience, 2020

A community-based transcriptomics classification and nomenclature of neocortical cell types To un... more A community-based transcriptomics classification and nomenclature of neocortical cell types To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Neuroinformatics, 2020

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and m... more Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties.
Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal
projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the
characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation
patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the
axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification
schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and
utilizing form-function principles in realistic neuronal reconstructions.

Scientific Reports, 2017

Computational modeling of signal propagation in neurons is critical to our understanding of basic... more Computational modeling of signal propagation in neurons is critical to our understanding of basic principles underlying brain organization and activity. Exploring these models is used to address basic neuroscience questions as well as to gain insights for clinical applications. The seminal Hodgkin Huxley model is a common theoretical framework to study brain activity. It was mainly used to investigate the electrochemical and physical properties of neurons. The influence of neuronal structure on activity patterns was explored, however, the rich dynamics observed in neurons with different morphologies is not yet fully understood. Here, we study signal propagation in fundamental building blocks of neuronal branching trees, unbranched and branched axons. We show how these simple axonal elements can code information on spike trains, and how asymmetric responses can emerge in axonal branching points. This asymmetric phenomenon has been observed experimentally but until now lacked theoretical characterization. Together, our results suggest that axonal morphological parameters are instrumental in activity modulation and information coding. The insights gained from this work lay the ground for better understanding the interplay between function and form in real-world complex systems. It may also supply theoretical basis for the development of novel therapeutic approaches to damaged nervous systems.

BMC Neuroscience, 2013

A. Series of five spikes before failure for a current stimulus of 1.025 mA/cm 2 along the axon, 2... more A. Series of five spikes before failure for a current stimulus of 1.025 mA/cm 2 along the axon, 20μm radius. B. Single spike followed by three failures for a current stimulus of 8.5 mA/cm 2 along the axon, 500μm radius. C. A chaotic response to a current stimulus of 4.064 mA/cm 2 along the axon, 238μm radius.

Neurons generate diverse patterns of activity for various functions. Revealing factors which dete... more Neurons generate diverse patterns of activity for various functions. Revealing factors which
determine neuronal firing patterns is fundamental to a better understanding of brain activity
and coding. Traditionally, the space clamp model has been used to investigate neuronal
electrical activity. In this paper, we study the Hodgkin–Huxley cable model, taking into consideration
axonal geometry. We examine the influence of morphology on neuronal activity,
exploring neuronal response to constant current stimuli injected into one end of fiber-like axons
of different lengths and radii. We demonstrate novel patterns of firing, including a finite
number of spikes and series followed by failures, and under some specific current stimulus
regimes, we also detect irregular behaviors. Our results illustrate various means in which the
pattern of activity may be regulated by axonal structure, suggesting this mechanism is instrumental
in information coding of physiological, as well as deforming pathological conditions.

bioRxiv, 2018

Classification of neurons into specific subtypes is essential for better understanding of brain f... more Classification of neurons into specific subtypes is essential for better understanding of brain function and information transmission.
Despite continuous progress, there is still no consensus regarding categorizing neuron taxonomy into proper subtypes.
Current morphology-based classification approaches largely rely on the dendritic tree structure or on the general axonal projection layout.
In this study, we support the use of a morphology-based classification approach, focusing on the axonal tree.
We demonstrate that utilizing the geometrical parameters of axonal tree structures significantly improves neuronal classification compared to the dendritic tree classification.
Furthermore, we used neuronal activity patterns to classify interneurons into subtypes as well.
Simulations of the activity along ramified axonal trees indicate that the axonal branching geometry may yield diverse responses in different subtrees.
The classification schemes introduced here can be utilized to robustly classify neuronal subtypes in a functionally relevant manner. Our results open the door for deducing functionality from anatomical data.