Microfluidic Compartmentalization Platforms for Single Cell Analysis

Luo, Xuhao; Chen, Jui-Yi; Ataei, Marzieh; Lee, Abraham P.

doi:10.3390/bios12020058

Cited by 15 publications

(16 citation statements)

References 119 publications

(163 reference statements)

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“…Perhaps more importantly, an effective disambiguation algorithm (we call this pair cleaning ) raises the possibility of purposefully overloading droplets in order to increase the total number of sequenced cells. It would also simplify sample preparation by reducing the necessity for mono-dispersed droplets [ 43 ]. Because our method uses clonal relationships to determine correct pairings, sequences in large families are more likely to be corrected than those in small clusters.…”

Section: Resultsmentioning

confidence: 99%

Inference of B cell clonal families using heavy/light chain pairing information

Ralph

Matsen

2022

PLoS Comput Biol

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Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.

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Section: Resultsmentioning

confidence: 99%

Inference of B cell clonal families using heavy/light chain pairing information

Ralph

Matsen

2022

PLoS Comput Biol

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show abstract

“…However, perhaps more importantly, an effective disambiguation algorithm (we call this pair cleaning ) raises the possibility of purposefully overloading droplets in order to increase the total number of sequenced cells. It would also simplify sample preparation by reducing the necessity for mono-dispersed droplets [37]. Because our method uses clonal relationships to determine correct pairings, sequences in large families are more likely to be corrected than those in small clusters.…”

Section: Resultsmentioning

confidence: 99%

“…We also note that we have used the pair cleaning algorithm with good results on (not-yet-published) non-10X data that flow-sorted cells into wells, but left multiple cells in some wells. Our method may also allow for simplified sample preparation, since the Poisson statistics determining cell-bead co-encapsulation often require very dilute suspensions to achieve mono-dispersion, which compromises efficiency [37].…”

Section: Discussionmentioning

confidence: 99%

Inference of B cell clonal families using heavy/light chain pairing information

Ralph

Matsen

2022

Preprint

View full text Add to dashboard Cite

Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the case of the light chain; the light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package (https://github.com/psathyrella/partis).

show abstract

“…We also note that we have used the pair cleaning algorithm with good results on (not-yet-published) non-10X data that flow-sorted cells into wells, but left multiple cells in some wells. Our method may also allow for simplified sample preparation, since the Poisson statistics determining cell-bead coencapsulation often require very dilute suspensions to achieve mono-dispersion, which compromises efficiency [37].…”

Section: Discussionmentioning

confidence: 99%